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Lyapunov-Based Control Methods for Neuromuscular Electrical Stimulation

Permanent Link: http://ufdc.ufl.edu/UFE0041961/00001

Material Information

Title: Lyapunov-Based Control Methods for Neuromuscular Electrical Stimulation
Physical Description: 1 online resource (153 p.)
Language: english
Creator: Sharma, Nitin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: adaptive, electrical, lyapunov, neural, nmes, nonlinear, rise, time
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Mechanical Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Neuromuscular electrical stimulation (NMES) is the application of a potential field across a muscle in order to produce a desired muscle contraction. NMES is a promising treatment that has the potential to restore functional tasks in persons with movement disorders. Towards this goal, the research objective in the dissertation is to develop NMES controllers that will enable a person's lower shank to track a continuous desired trajectory (or constant setpoint). A nonlinear musculoskeletal model is developed in Chapter 2 which describes muscle activation and contraction dynamics and body segmental dynamics during NMES. The definitions of various components in the musculoskeletal dynamics are provided but are not required for control implementation. Instead, the structure of the relationships is used to define properties and make assumptions for control development. A nonlinear control method is developed in Chapter 3 to control the human quadriceps femoris muscle undergoing non-isometric contractions. The developed controller does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model in the presence of bounded nonlinear disturbances. The performance of the controller is demonstrated through a series of closed-loop experiments on healthy normal volunteers. The experiments illustrate the ability of the controller to enable the shank to follow trajectories with different periods and ranges of motion, and also track desired step changes with changing loads. The most promising and popular control methods for NMES are neural network (NN)-based methods since these methods can be used to learn nonlinear muscle force to length and velocity relationship, and the inherent unstructured and time-varying uncertainties in available models. Further efforts in Chapter 3 focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded (UUB) stability). Specifically, a NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired time-varying limb trajectory (i.e., non-isometric contractions). The added value of incorporating a NN feedforward term is illustrated through experiments on healthy normal volunteers that compare the developed controller with the pure RISE-based feedback controller. A pervasive problem with current NMES technology is the rapid onset of the unavoidable muscle fatigue during NMES. In closed-loop NMES control, disturbances such as muscle fatigue are often tackled through high-gain feedback which can overstimulate the muscle which further intensifies the fatigue onset. In Chapter 4, a NMES controller is developed that incorporates the effects of muscle fatigue through an uncertain function of the calcium dynamics. A NN-based estimate of the fatigue model mismatch is incorporated in a nonlinear controller through a backstepping method to control the human quadriceps femoris muscle undergoing non-isometric contractions. The developed controller is proven to yield UUB stability for an uncertain nonlinear muscle model in the presence of bounded nonlinear disturbances (e.g., spasticity, delays, changing load dynamics). Simulations are provided to illustrate the performance of the proposed controller. Continued efforts will focus on achieving asymptotic tracking versus the UUB result, and on validating the controller through experiments. Another impediment in NMES control is the presence of input or actuator delay. Control of nonlinear systems with actuator delay is a challenging problem because of the need to develop some form of prediction of the nonlinear dynamics. The problem becomes more difficult for systems with uncertain dynamics. Motivated to address the input delay problem in NMES control and the absence of non-model based controllers for a nonlinear system with input delay in the literature, tracking controllers are developed in Chapter 5 for an Euler-Lagrange system with time-delayed actuation, parametric uncertainty, and additive bounded disturbances. One controller is developed under the assumption that the inertia is known, and a second controller is developed when the inertia is unknown. For each case a predictor-like method is developed to address the time delay in the control input. Lyapunov-Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-global UUB tracking. Extensive experiments show better performance compared to traditional PD/PID controller as well as robustness to uncertainty in the inertia matrix and time delay value. Experiments are performed on healthy normal individuals to show the feasibility, performance, and robustness of the developed controller. In addition to efforts focussed on input delayed nonlinear systems, a parallel motivation exists to address another class of time delayed systems which consist of nonlinear systems with unknown state delays. A continuous robust adaptive control method is designed in Chapter 6 for a class of uncertain nonlinear systems with unknown constant time-delays in the states. Specifically, the robust adaptive control method, a gradient-based desired compensation adaptation law (DCAL), and a Lyapunov-Kravoskii (LK) functional-based delay control term are utilized to compensate for unknown time-delays, linearly parameterizable uncertainties, and additive bounded disturbances for a general nonlinear system. Despite these disturbances, a Lyapunov-based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory. Chapter 7 concludes the dissertation with a discussion of the developed contributions and future efforts.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nitin Sharma.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Dixon, Warren E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041961:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041961/00001

Material Information

Title: Lyapunov-Based Control Methods for Neuromuscular Electrical Stimulation
Physical Description: 1 online resource (153 p.)
Language: english
Creator: Sharma, Nitin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: adaptive, electrical, lyapunov, neural, nmes, nonlinear, rise, time
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Mechanical Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Neuromuscular electrical stimulation (NMES) is the application of a potential field across a muscle in order to produce a desired muscle contraction. NMES is a promising treatment that has the potential to restore functional tasks in persons with movement disorders. Towards this goal, the research objective in the dissertation is to develop NMES controllers that will enable a person's lower shank to track a continuous desired trajectory (or constant setpoint). A nonlinear musculoskeletal model is developed in Chapter 2 which describes muscle activation and contraction dynamics and body segmental dynamics during NMES. The definitions of various components in the musculoskeletal dynamics are provided but are not required for control implementation. Instead, the structure of the relationships is used to define properties and make assumptions for control development. A nonlinear control method is developed in Chapter 3 to control the human quadriceps femoris muscle undergoing non-isometric contractions. The developed controller does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model in the presence of bounded nonlinear disturbances. The performance of the controller is demonstrated through a series of closed-loop experiments on healthy normal volunteers. The experiments illustrate the ability of the controller to enable the shank to follow trajectories with different periods and ranges of motion, and also track desired step changes with changing loads. The most promising and popular control methods for NMES are neural network (NN)-based methods since these methods can be used to learn nonlinear muscle force to length and velocity relationship, and the inherent unstructured and time-varying uncertainties in available models. Further efforts in Chapter 3 focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded (UUB) stability). Specifically, a NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired time-varying limb trajectory (i.e., non-isometric contractions). The added value of incorporating a NN feedforward term is illustrated through experiments on healthy normal volunteers that compare the developed controller with the pure RISE-based feedback controller. A pervasive problem with current NMES technology is the rapid onset of the unavoidable muscle fatigue during NMES. In closed-loop NMES control, disturbances such as muscle fatigue are often tackled through high-gain feedback which can overstimulate the muscle which further intensifies the fatigue onset. In Chapter 4, a NMES controller is developed that incorporates the effects of muscle fatigue through an uncertain function of the calcium dynamics. A NN-based estimate of the fatigue model mismatch is incorporated in a nonlinear controller through a backstepping method to control the human quadriceps femoris muscle undergoing non-isometric contractions. The developed controller is proven to yield UUB stability for an uncertain nonlinear muscle model in the presence of bounded nonlinear disturbances (e.g., spasticity, delays, changing load dynamics). Simulations are provided to illustrate the performance of the proposed controller. Continued efforts will focus on achieving asymptotic tracking versus the UUB result, and on validating the controller through experiments. Another impediment in NMES control is the presence of input or actuator delay. Control of nonlinear systems with actuator delay is a challenging problem because of the need to develop some form of prediction of the nonlinear dynamics. The problem becomes more difficult for systems with uncertain dynamics. Motivated to address the input delay problem in NMES control and the absence of non-model based controllers for a nonlinear system with input delay in the literature, tracking controllers are developed in Chapter 5 for an Euler-Lagrange system with time-delayed actuation, parametric uncertainty, and additive bounded disturbances. One controller is developed under the assumption that the inertia is known, and a second controller is developed when the inertia is unknown. For each case a predictor-like method is developed to address the time delay in the control input. Lyapunov-Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-global UUB tracking. Extensive experiments show better performance compared to traditional PD/PID controller as well as robustness to uncertainty in the inertia matrix and time delay value. Experiments are performed on healthy normal individuals to show the feasibility, performance, and robustness of the developed controller. In addition to efforts focussed on input delayed nonlinear systems, a parallel motivation exists to address another class of time delayed systems which consist of nonlinear systems with unknown state delays. A continuous robust adaptive control method is designed in Chapter 6 for a class of uncertain nonlinear systems with unknown constant time-delays in the states. Specifically, the robust adaptive control method, a gradient-based desired compensation adaptation law (DCAL), and a Lyapunov-Kravoskii (LK) functional-based delay control term are utilized to compensate for unknown time-delays, linearly parameterizable uncertainties, and additive bounded disturbances for a general nonlinear system. Despite these disturbances, a Lyapunov-based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory. Chapter 7 concludes the dissertation with a discussion of the developed contributions and future efforts.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Nitin Sharma.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Dixon, Warren E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041961:00001


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LYAPUNOV-BASED CONTROL METHODS FOR NEUROMUSCULAR ELECTRICAL
STIMULATION



















By

NITIN SHARMA


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010

































S2010 Nitin Sharma





















To my loving wife, Deepti, my dear parents, Neena and Balwinder Sharma, and my

affectionate sister, Nitika for their unwavering support









ACKNOWLEDGMENTS

I would like to express sincere gratitude to my advisor, Dr. Warren E. Dixon, for

giving me the opportunity to work with him. I thank him for exposing me to vast and

exciting research area of nonlinear control and motivating me to work on Neuromuscular

Electrical Stimulation (NMES) control problem. I have learnt tremendously from his

experience and appreciate his significant role in developing my professional skills and

contributing to my academic success.

I would also like to thank my co-advisor Dr. ('!n -' Gregory for answering my queries

related to muscle physiology and for guiding me in building correct protocols during

NMES experiments. I also appreciate my committee members Dr. Scott Banks, Dr. Carl

D. Crane III and Dr. Jacob Hammer for the time and help they provided.

I would like to thank my colleagues for their support and appreciate their steadfast

volunteering in NMES experiments.

I would like to thank my wife for her love and patience. Also, I would like to attribute

my overall success to my mother who took her time and effort to teach me during my

childhood. Finally, I would like to thank my father for his belief in me.









TABLE OF CONTENTS


page


ACKNOW LEDGMENTS .................................

LIST O F TABLES . . .

LIST OF FIGURES . . .

A B ST R A C T . . .

CHAPTER

1 INTRODUCTION ..................................

1.1 Motivation and Problem Statement ......................
1.2 C contributions . . .

2 MUSCLE ACTIVATION AND LIMB MODEL .................


3 NONLINEAR NEUROMUSCULAR ELECTRIC
TRACKING CONTROL OF A HUMAN LIME


Introduction .. ............
Control Development .........
Nonlinear NMES Control of a Human
of Error (RISE) method .......


Limr


3.3.1 Stability Analysis .........
3.3.2 Experimental Results .......
3.3.2.1 Testbed and protocol
3.3.2.2 Results and discussion
3.3.3 Conclusion .............
3.4 Modified Neural Network-based Electrical
Tracking . .
3.4.1 Open-Loop Error System .....
3.4.2 Closed-Loop Error System .
3.4.3 Stability Analysis .. .......
3.4.4 Experimental Results .......
3.4.4.1 Testbed and protocol
3.4.4.2 Results and discussion
3.4.5 Limitations .. .........
3.4.6 Conclusion .. ...........


CAL STIMULATION (NMES)




ib via Robust Integral of Signum







Stimulation for Human Limb


4 NONLINEAR CONTROL OF NMES: INCORPORATING FATIGUE AND
CALCIUM DYNAMICS .. .........................

4.1 Introduction . . .
4.2 Muscle Activation and Limb Model .. ..................









4.3 Control Development .................. ........... .. 76
4.3.1 Open-Loop Error System .................. ... .. 77
4.3.2 Closed-Loop Error System .................. .. 79
4.3.3 Backstepping Error System ...... .......... .. 81
4.4 Stability Analysis ............... ............ .. 81
4.5 Simulations ............... ............ .. 84
4.6 Conclusion ................ .............. .. 85

5 PREDICTOR-BASED CONTROL FOR AN UNCERTAIN EULER-LAGRANGE
SYSTEM WITH INPUT DELAY .................. ........ .. 89

5.1 Introduction .................. ................ .. 89
5.2 Dynamic Model and Properties .................. .... .. 90
5.3 Control Development .................. ........... .. 91
5.3.1 Objective. .................. ........... .. 91
5.3.2 Control development given a Known Inertia Matrix ... 91
5.3.3 Control development with an Unknown Inertia Matrix ... 97
5.4 Experimental Results and Discussion ..... ... 103
5.5 Delay compensation in NMES through Predictor-based Control ...... 106
5.5.1 Experiments: Input Delay Characterization 107
5.5.2 Experiments: PD Controller with Delay Compensation ...... .113
5.6 Conclusion .. .... .. 117

6 RISE-BASED ADAPTIVE CONTROL OF AN UNCERTAIN NONLINEAR
SYSTEM WITH UNKNOWN STATE DELAYS ..... 120

6.1 Introduction .................. ................ .. 120
6.2 Problem Formulation .................. ........... 120
6.3 Error System Development .................. ........ .. 121
6.4 Stability Analysis .................. ........... .. 125
6.5 Simulations .................. ................ .. 130
6.6 Conclusion .. .... .. 131

7 CONCLUSION AND FUTURE WORK ................... 135

7.1 Conclusion .. .... .. 135
7.2 Future W ork .................. ................ .. 136

APPENDIX

A PREDICTOR-BASED CONTROL FOR AN UNCERTAIN EULER-LAGRANGE
SYSTEM WITH INPUT DELAY ................... ...... .. 139

B RISE-BASED ADAPTIVE CONTROL OF AN UNCERTAIN NONLINEAR
SYSTEM WITH UNKNOWN STATE DELAYS ..... ..... 141

REFERENCES .................. ................ .. .. 143









BIOGRAPHICAL SKETCH ................... .......... 153









LIST OF TABLES


Table page

3-1 Tabulated results indicate that the test subject was not learning the desired
trajectory since the RMS errors are relatively equal for each trial. ... 40

3-2 Experimental results for two period desired trajectory .............. .41

3-3 Summarized experimental results for multiple, higher frequencies and higher
range of m otion. .. .. .. ... .. .. .. ... ... ... .. ... .. 43

3-4 Summarized experimental results and P values of one tailed paired T-test for a
1.5 second period desired trajectory. .................. ..... 63

3-5 Summarized experimental results and P values of one tailed paired T-test for
dual periodic (4-6 second) desired trajectory. .................. 66

3-6 Experimental results for step response and changing loads .... 66

3-7 The table shows the RMS errors during extension and flexion phase of the leg
movement across different subjects, .................. ..... .. 71

5-1 Summarized experimental results of traditional PID/PD controllers and the PID/PD
controllers with delay compensation. .................. ..... 108

5-2 Results compare performance of the PD controller with delay compensation,
when the B gain matrix is varied from the known inverse inertia matrix. 108

5-3 Experimental results when the input delay has uncertainty. The input delay
value was selected as 100 ms. .................. .. .. .. 109

5-4 Summarized input delay values of a 1 1, i.,l!: individual across different stimulation
parameters .................. ................... .. 116

5-5 Table compares the experimental results obtained from the traditional PD controller
and the PD controller with /. /.,;/ compensation. ................ 119









LIST OF FIGURES


Figure page

2-1 Muscle activation and limb model. .................. ..... 25

2-2 The left image illustrates a person's left leg in a relaxed state. .... 27

3-1 Top plots: Actual left limb trajectory of a subject (solid line) versus the desired
two periodic trajectory (dashed line) input. .................. 42

3-2 Top plot: Actual limb trajectory (solid line) versus the desired triple periodic
trajectory (dashed line). ............... ........... 43

3-3 Top plot: Actual limb trajectory (solid line) versus the desired constant period
(2 sec) trajectory (dashed line). .................. ........ .. 44

3-4 Top plot: Actual limb trajectory (solid line) versus the triple periodic desired
trajectory with higher range of motion (dashed line). 45

3-5 Top plot: Actual limb trajectory (solid line) versus the desired constant period
(6 sec) trajectory (dashed line). .................. ........ .. 46

3-6 Top plot : Actual limb trajectory (solid line) versus desired step trajectory (dashed
line). .... .. .. .. 47

3-7 The top plot shows the actual limb trajectory (solid line) obtained from the RISE
controller versus the desired 1.5 second period desired trajectory (dashed line). 61

3-8 The top plot shows the actual limb trajectory (solid line) obtained from the NN+RISE
controller versus the desired 1.5 second period desired trajectory (dashed line). 62

3-9 The top plot shows the actual limb trajectory (solid line) obtained from the RISE
controller versus the dual periodic desired trajectory (dashed line). ...... ..64

3-10 The top plot shows the actual limb trajectory (solid line) obtained from the NN+RISE
controller versus the dual periodic desired trajectory (dashed line). ...... ..65

3-11 Experimental plots for step change and load addition obtained from NN+RISE
controller .................. ............. .. .. 67

3-12 Initial sitting position during sit-to-stand experiments. The knee-angle was measured
using a goniometer attached around the knee-axis of the subject's leg. 68

3-13 The top plot shows the actual leg angle trajectory (solid line) versus desired
trajectory (dotted line) obtained during the standing experiment. ... 69

4-1 An uncertain fatigue model is incorporated in the control design to address muscle
fatigue. Best guess estimates are used for unknown model parameters. 76









4-2 Top plot shows the knee angle error for a 6 second period trajectory using the
proposed controller. .................. ... .......... 84

4-3 Top plot shows the knee angle error for a 2 second period trajectory using the
proposed controller. .................. ... .......... 85

4-4 Top plot shows the knee angle error for a 6 second period trajectory using the
RISE controller .................. ................. .. 86

4-5 Top plot shows the knee angle error for a 2 second period trajectory using the
RISE controller .................. ................. .. 87

4-6 RISE controller with fatigue in the dynamics ................ 87

4-7 Performance of the proposed controller .................. ...... 88

4-8 Fatigue variable .................. ............... .. .. 88

5-1 Experimental testbed consiting of a 2-link robot. The input delay in the system
was artificially inserted in the control software. ................ 103

5-2 The plot shows three torque terms .................. ..... 107

5-3 The top-left and bottom-left plots show the errors of Link 1 and Link 2 ..... ..110

5-4 The top-left and bottom-left plots show the torques of Link 1 and Link 2 111

5-5 Typical input delay during NMES in a healthy individual. ..... 112

5-6 Average input d,1 iv values across different frequencies. ............. .113

5-7 Average input d,1 iv values across different voltages. ............... ..114

5-8 Average input d,1 iv values across different pulsewidths. ............. .115

5-9 Top plot: Actual limb trajectory of a subject (solid line) versus the desired trajectory
(dashed line) input obtained with the PD controller with /. /.,;/ compensation... 117

5-10 Top plot: Actual limb trajectory of a subject (solid line) versus the desired trajectory
(dashed line) input ............... ........... .. .. 118

6-1 Tracking error for the case r = 3 s. ............... .. ..... 132

6-2 Control input for the case r = 3 s ................. ..... 132

6-3 Parameter estimates for the case T = 3 s. ................ ...... 133

6-4 Tracking error for the case T = 10 s. .............. .... 133

6-5 Control input for the case T = 10 s. ................ .. ... 134

6-6 Parameter estimates for the case T = 10 s. ............. 134









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

LYAPUNOV-BASED CONTROL METHODS FOR NEUROMUSCULAR ELECTRICAL
STIMULATION

By

Nitin Sharma

August 2010

('C! i': Warren E. Dixon
Major: Mechanical Engineering

Neuromuscular electrical stimulation (NMES) is the application of a potential field

across a muscle in order to produce a desired muscle contraction. NMES is a promising

treatment that has the potential to restore functional tasks in persons with movement

disorders. Towards this goal, the research objective in the dissertation is to develop NMES

controllers that will enable a person's lower shank to track a continuous desired trajectory

(or constant setpoint).

A nonlinear musculoskeletal model is developed in C!i lpter 2 which describes muscle

activation and contraction dynamics and body segmental dynamics during NMES. The

definitions of various components in the musculoskeletal dynamics are provided but are

not required for control implementation. Instead, the structure of the relationships is used

to define properties and make assumptions for control development.

A nonlinear control method is developed in C(i lpter 3 to control the human

quadriceps femoris muscle undergoing non-isometric contractions. The developed

controller does not require a muscle model and can be proven to yield .,-i~,iiil'I ic stability

for a nonlinear muscle model in the presence of bounded nonlinear disturbances. The

performance of the controller is demonstrated through a series of closed-loop experiments

on healthy normal volunteers. The experiments illustrate the ability of the controller to

enable the shank to follow trajectories with different periods and ranges of motion, and

also track desired step changes with changing loads.









The most promising and popular control methods for NMES are neural network

(NN)-based methods since these methods can be used to learn nonlinear muscle force

to length and velocity relationship, and the inherent unstructured and time-varying

uncertainties in available models. Further efforts in C'! lpter 3 focus on the use of a NN

feedforward controller that is augmented with a continuous robust feedback term to yield

an .i-i-,ii !ll ic result (in lieu of typical uniformly ultimately bounded (UUB) stability).

Specifically, a NN-based controller and Lyapunov-based stability a'i ,1 i-i; are provided

to enable semi-global .i-vmptotic tracking of a desired time-varying limb trajectory (i.e.,

non-isometric contractions). The added value of incorporating a NN feedforward term is

illustrated through experiments on healthy normal volunteers that compare the developed

controller with the pure RISE-based feedback controller.

A pervasive problem with current NMES technology is the rapid onset of the

unavoidable muscle fatigue during NMES. In closed-loop NMES control, disturbances such

as muscle fatigue are often tackled through high-gain feedback which can overstimulate

the muscle which further intensifies the fatigue onset. In C'! lpter 4, a NMES controller

is developed that incorporates the effects of muscle fatigue through an uncertain function

of the calcium dynamics. A NN-based estimate of the fatigue model mismatch is

incorporated in a nonlinear controller through a backstepping method to control the

human quadriceps femoris muscle undergoing non-isometric contractions. The developed

controller is proven to yield UUB stability for an uncertain nonlinear muscle model

in the presence of bounded nonlinear disturbances (e.g., spasticity, d-.1 'v, changing

load dynamics). Simulations are provided to illustrate the performance of the proposed

controller. Continued efforts will focus on achieving .I-vmptotic tracking versus the UUB

result, and on validating the controller through experiments.

Another impediment in NMES control is the presence of input or actuator delay.

Control of nonlinear systems with actuator delay is a challenging problem because of

the need to develop some form of prediction of the nonlinear dynamics. The problem









becomes more difficult for systems with uncertain dynamics. Motivated to address the

input delay problem in NMES control and the absence of non-model based controllers for

a nonlinear system with input delay in the literature, tracking controllers are developed

in C'!i lpter 5 for an Euler-Lagrange system with time-d, 1 i-, .1 actuation, parametric

uncertainty, and additive bounded disturbances. One controller is developed under

the assumption that the inertia is known, and a second controller is developed when

the inertia is unknown. For each case a predictor-like method is developed to address

the time delay in the control input. Lyapunov-Krasovskii functionals are used within

a Lyapunov-based stability analysis to prove semi-global UUB tracking. Extensive

experiments show better performance compared to traditional PD/PID controller as well

as robustness to uncertainty in the inertia matrix and time delay value. Experiments

are performed on l. ,lr i!:r normal individuals to show the feasibility, performance, and

robustness of the developed controller.

In addition to efforts focused on input d. 1 i, .1 nonlinear systems, a parallel

motivation exists to address another class of time d. 1 -i, i1 systems which consist of

nonlinear systems with unknown state d.-1 iv. A continuous robust adaptive control

method is designed in ('!i lpter 6 for a class of uncertain nonlinear systems with unknown

constant time-d--. 1- in the states. Specifically, the robust adaptive control method, a

gradient-based desired compensation adaptation law (DCAL), and a Lyapunov-Kravoskii

(LK) functional-based delay control term are utilized to compensate for unknown

time-d-. 1 -, linearly parameterizable uncertainties, and additive bounded disturbances

for a general nonlinear system. Despite these disturbances, a Lyapunov-based analysis

is used to conclude that the system output .,-i-!ii11i .1 ically tracks a desired time varying

bounded trajectory.

('!i lpter 7 concludes the dissertation with a discussion of the developed contributions

and future efforts.









CHAPTER 1
INTRODUCTION

1.1 Motivation and Problem Statement

Neuromuscular electrical stimulation (NMES) is the application of a potential field

across a muscle to produce a desired muscle contraction (for functional tasks, NMES

is described as functional electrical stimulation (FES)). Efforts in NMES facilitate

improved limb control and functionality for patients with stroke, spinal cord injuries,

and other neurological impairments [1, 2]. Although most NMES procedures in physical

therapy clinics consist of tabulated open-loop application of electrical stimulation, a

significant market exists for the development of noninvasive closed-loop methods.

However, the application and development of NMES control have been stymied by

several technical challenges. Specifically, due to a variety of uncertainties in muscle

physiology (e.g., temperature, pH, and architecture), predicting the exact contraction

force exerted by the muscle is difficult. One cause of this difficulty is that there is an

unknown mapping between the generated muscle force and stimulation parameters.

There are additional problems with delivering consistent stimulation energy to the

muscle due to a variety of factors including: muscle fatigue, input delay, electrode

placement, hyperactive somatosensory reflexes, inter- and intra-subject variability in

muscle properties, changing muscle geometry under the electrodes in non-isometric

conditions, percentage of subcutaneous body fat, overall body hydration, etc.

Given the uncertainties in the structure of the muscle model and the parametric

uncertainty for specific muscles, some investigators have explored various linear PID-based

methods (cf. [3-8] and the references therein). Typically, these approaches have only

been empirically investigated and no analytical stability analysis has been developed that

provides an indication of the performance, robustness or stability of these control methods.

The development of a stability analysis for previous PID-based NMES controllers has been

evasive because of the fact that the governing equations for a muscle contraction/limb









motion are nonlinear with unstructured uncertainties. Some efforts have focused on

analytical control development for linear controllers (e.g., [6, 9, 10]); however, the

governing equations are typically linearized to accommodate a gain scheduling or linear

optimal controller approach.

Motivated to develop effective NMES control in light of these challenges, the first

result in ('! Ilpter 3 develops an open-loop error system for a general uncertain nonlinear

muscle model based on available analytical and empirical data [11, 12]) that facilitates

the development of a new continuous feedback method (coined RISE for Robust Integral

of the Sign of the Error). Through this error-system development, the continuous RISE

controller is proven (through a Lyapunov-based stability analysis) to yield an .,-vmptotic

stability result despite the uncertain nonlinear muscle model and the presence of additive

bounded disturbances (e.g., muscle spasticity, fatigue, changing loads in functional tasks,

and unmodeled muscle behavior).

Seminal work in [13-18] continue to inspire new investigations (cf. [19-26] and

the references therein) in neural network (NN)-based NMES control development.

One motivation for NN-based controllers is the desire to augment feedback methods

with an adaptive element that can adjust to the uncertain muscle model, rather than

only relying on feedback to dominate the uncertainty based on worse case scenarios.

NN-based control methods have attracted more attention in NMES than other adaptive

feedforward methods because of the nature of the unstructured uncertainty and the

universal approximation property of NNs. However, since NNs can only approximate a

function within some residual approximation error, all previous NN-based controllers yield

uniformly ultimately bounded stability (i.e., the errors converge to a region of bounded

steady-state error).

The result in the third section of ('! Ilpter 3 focuses on the development of a

RISE-based NMES controller and the associated analytical stability il i ,i--- that

yields .,-i-! iiil'itic tracking in the presence of a nonlinear uncertain muscle model with









nonvanishing additive disturbances. This result uses feedback and an implicit learning

mechanism to dominate uncertainty and disturbances. However, the RISE method as well

as the previous linear feedback methods inherently rely on high gains or high frequency

to dominate the model uncertainty, potentially resulting in overstimulation. Recent

results from general control systems literature [27] indicate that the RISE-based feedback

structure can be augmented with a NN feedforward term to yield .,-i- !,.l .l ic tracking

for some classes of systems. Based on these general results, an extension is provided in

the fourth section of C'! lpter 3 where the RISE-based method is modified with a NN to

develop a new NMES controller for the uncertain muscle model.

While efforts in C'! lpter 3, provide an inroad to the development of analytical

NMES controllers for the nonlinear muscle model, these results do not account for muscle

fatigue, which is a primary factor to consider to yield some functional results in many

rehabilitation applications. Heuristically, muscle fatigue is a decrease in the muscle

force output for a given input and is a complex, multifactorial phenomenon [28-30]. In

general, some of the factors associated with the onset of fatigue are failure of excitation

of motor neurons, impairment of action potential propagation in the muscle membrane

and conductivity of sarcoplasmic reticulum to Ca2+ ion concentration, and the change in

concentration of catabolites and metabolites [31]. Factors such as the stimulation method,

muscle fibre composition, state of training of the muscle, and the duration and task to

be performed have been noticed to affect fatigue during NMES. Given the impact of

fatigue effects during NMES, researchers have proposed different stimulation strategies

[30, 32, 33] to delay the onset of fatigue such as choosing different stimulation patterns

and parameters, improving fatigue resistance through muscle retraining, sequential

stimulation, and size order recruitment.

Controllers can be designed with some feedforward knowledge to approximate the

fatigue onset or employ some assumed mathematical model of the fatigue in the control

design. Researchers in [34-38] developed various mathematical models for fatigue. In









[34], a musculotendon model for a quadriceps muscle undergoing isometric contractions
during functional electrical stimulation (FES) was proposed. The model incorporated

fatigue based on the intracellular pH level where the fatigue parameters for a typical

subject were found through metabolic information, experimentation and curve fitting. A

more general mathematical model for dynamic fatigue defined as a function of normalized

muscle activation variable (Ca2+ dynamics) was proposed in [35, 36]. The fatigue was

introduced as a fitness function that varies according to the increase or decrease in muscle

activation during electrical stimulation. The fatigue time parameters were estimated

from stimulation experiments. Models in [37] and [38] predict force due to the effect of

stimulation patterns and resting times with changing physiological conditions, where

model parameterization required investigating experimental forces generated from a

standardized stimulation protocol. Although these mathematical models for fatigue

prediction are present in literature, few researchers have utilized these assumed fatigue

models in closed-loop NMES control. Results in [36] and [39] use the fatigue model

proposed in [35] and [36] for a FES controller, where patient specific parameters (e.g.

fatigue time constants) are assumed to be known along with exact model knowledge of the

calcium dynamics. The difficulty involved in the control design using calcium dynamics or

intracellular pH level is that these states cannot be measured easily for real-time control.

Therefore, these states (calcium dynamics or pH level) are modeled as a first or second

order ordinary differential equation (cf., [34, 36, 39]) and the parameters in the equations

are estimated from experimentation or are based on data from past studies.

The focus of Chapter 4 is to address muscle fatigue by incorporating an uncertain

fatigue model (i.e., the model developed in [35]) in the NMES controller. The uncertain

fatigue model is defined as a function of a normalized muscle activation variable. The

normalized muscle activation variable denotes the calcium (Ca2+ ion) dynamics which

act as an intermediate variable between contractile machinery and external stimulus.

The calcium dynamics are modeled as a first order differential equation based on [6] and









[39]. A backstepping approach is utilized to design virtual control input that consists
of NN-based feedforward signal and feedback signal. The developed controller yields a

uniformly ultimately bounded stability result given an unknown nonlinear muscle model

with uncertain fatigue and calcium dynamics.

Another technical challenge that hampers the satisfactory NMES control performance

is electromechanical delay in muscle force generation which is defined as the difference in

time from the arrival of action potential at the neuromuscular junction to the development

of tension in the muscle [8]. In NMES control, the electromechanical delay is modeled as

an input delay in the musculoskeletal dynamics [6] and occurs due to finite conduction

velocities of the chemical ions in the muscle in response to the external electrical input

[36]. Input delay can cause performance degradation as was observed during NMES

experimental trials on volunteer subjects with RISE and NN+RISE controllers and has

also been reported to potentially cause instability during human stance experiments with

NMES [40]. Time delay in the control input (also known as dead time, or input delay)

is a pervasive problem in control applications other than NMES control. C('. 111- I1 and

combustion processes, telerobotic systems, vehicle platoons, and communication networks

[41-44] often encounter d-1i-, in the control input. Such d--1 are often attributed to

sensor measurement delay, transport lags, communication d4-1 i- or task prioritization,

and can lead to poor performance and potential instability.

Motivated by performance and stability problems, various methods have been

developed for linear systems with input d (1civ{ (cf. [45-57] and the references therein). As

discussed in [45, 46], an outcome of these results is the development and use of prediction

techniques such as Artstein model reduction [48], finite spectrum assignment [51], and

continuous pole placement [58]. The concept of predictive control originated from classic

Smith predictor methods [59]. The Smith predictor requires a plant model for output

prediction and has been widely studied and modified for control purposes (cf. [60-67]

and references therein). However, the Smith predictor does not provide good closed-loop









performance in the presence of model mismatch and can only be applied for stable plants

[42, 46]. Contrary to the Smith predictor, finite spectrum assignment or Artstein model

reduction techniques and their extensions [47-53, 68-71] can be applied to unstable or

multivariable linear plants. These predictor-based methods utilize finite integrals over past

control values to reduce the d. 1 i,, .1 system to a delay free system.

Another approach to develop predictive controllers is based on the fact that input

delay systems can be represented by hyperbolic partial differential equations (cf. [45, 46]

and references therein). This fact is exploited in [54-57] to design controllers for actuator

d. 1 i-, .1 linear systems. These novel methods model the time d. 1 li .1 system as an

ordinary differential equation (ODE)-partial differential equation (PDE) cascade where

the non-d, 1 .i, d1 input acts at the PDE boundary. The controller is then designed by

employing a backstepping type approach for PDE control [72].

Predictor techniques have also been extended to adaptive control of unknown linear

plants in [41, 56, 73]. In [41, 73] the controller utilizes a modified Smith predictor type

structure to achieve a semi-global result. In [56] (and the companion paper [55]), a global

adaptive controller is developed that compensates for uncertain plant parameters and a

possibly large unknown delay.

In comparison to input d. 1 l' .1 linear systems, fewer results are available for nonlinear

systems. Approaches for input d, 1 -i, .1 nonlinear systems such as [74, 75] utilize a Smith

predictor-based globally linearizing control method and require a known nonlinear

plant model for time delay compensation. In [42], a specific technique is developed for a

telerobotic system with constant input and feedback d,41-i where a Smith predictor for

a locally linearized subsystem is used in combination with a neural network controller

for a remotely located uncertain nonlinear plant. In [76], an approach to construct

Lyapunov-Krasovskii (LK) functionals for input d. 1 i- .'1 nonlinear system in feedback

form is provided, and the control method in [77] utilizes a composite Lyapunov function

containing an integral cross term and LK functional for stabilizing nonlinear cascade









systems, where time d 1 iv can be either in the input or the states. The robustness of input

to state stabilizability is proven in [78] for nonlinear finite-dimensional control systems

in presence of small input d.- 1i- by utilizing a Razumikhin-type theorem. In [79], the

backstepping approach that utilizes ODE-PDE cascade transformation for input d. 1 .i1 ,

systems is extended to a scalar nonlinear system with actuator delay of unrestricted

length. However, to the best of our knowledge, no attempt has been made towards

stabilizing an input d, 1 '1 nonlinear system with parametric uncertainty and/or additive

bounded disturbances.

Motivated by the lack of NMES controllers that compensate for input delay and

the desire to develop non-model based controllers for nonlinear systems with input delay

C'! lpter 5 focuses on the development of a tracking controller for an uncertain nonlinear

Euler-Lagrange system with input delay. The input time delay is assumed to be a known

constant and can be arbitrary large. The dynamics are assumed to contain parametric

uncertainty and additive bounded disturbances. The first developed controller is based on

the assumption that the mass inertia is known, whereas the second controller is based on

the assumption that the mass inertia is unknown. The key contributions of this effort is

the design of a d. 1 li compensating auxiliary signal to obtain a time delay free open-loop

error system and the construction of LK functionals to cancel time d,1 li. 1 terms. The

auxiliary signal leads to the development of a predictor-based controller that contains

a finite integral of past control values. This d, 1 li,. 1 state to d, 1 li free transformation

is analogous to the Artstein model reduction approach, where a similar predictor-based

control is obtained. LK functionals containing finite integrals of control input values

are used in a Lyapunov-based analysis that proves the tracking errors are semi-global

uniformly ultimately bounded.

Another class of time-d, 1 .i, systems which are also endemic to engineering systems

and can cause degraded control performance and make closed-loop stabilization difficult

are systems with state d.-1 'i-. In time-d, 1 .i, d systems, the dynamics not only depends









on the current system states but also depends on the past state values. These systems

occur in many industrial and manufacturing systems (e.g., metal cutting process, rolling

mill, and chemical processes [46, 80].) A desire parallel to NMES research existed to

address this class of time delay systems. Various controllers have been developed to

address time-delay induced performance and stability issues as described in the survey

papers [45, 46] and in recent results that target control of uncertain systems with state

d-.1i- (cf. [80-86] and references therein). Control synthesis and stability analysis

methods for nonlinear time-d, 1 i,- d systems are often based on Lyapunov techniques in

conjunction with a Lyapunov-Kravoskii (LK) functional (cf. [82, 83, 85, 87]). For example,

in [82], an iterative procedure utilizing LK functionals for robust stabilization of a class

of nonlinear systems with triangular structure is developed. However, as stated in [88],

the controller cannot be constructed from the given iterative procedure. Semi-global

uniformly ultimately bounded (SUUB) results have been developed for time-d, 1 li,

nonlinear systems [83, 85] by utilizing neural network-based control, where appropriate LK

functionals are utilized to remove time d. 1 -i, i states. A discontinuous adaptive controller

was recently developed in [87] for a nonlinear system with an unknown time delay to

achieve a UUB result with the aid of LK functionals. However, controllers designed in

[83, 87] can become singular when the controlled state reaches zero and an ad hoc control

strategy is proposed to overcome the problem. Moreover, as stated in [89] and [90], the

control design procedure described in [85] cannot be generalized for nth order nonlinear

systems.

Sliding mode control (SMC) has also been utilized for time d, 1 1 systems in [80,

91-94]. However, utilizing SMC still poses a challenging design and computation problem

when d,-1 -- are present in states [45, 46]. Moreover, the discontinuous sign function

present in SMC controller often gives rise to the undesirable chattering phenomenon

during practical applications. To overcome the limitations of discontinuity in SMC, a

continuous adaptive sliding mode strategy is designed in [95] for nonlinear plants with









unknown state d.-1 i- where an LK functional along with a discontinuous Lyapunov

function is proposed for the stability analysis.

The development in ('! Ilpter 6 is motivated by the lack of continuous robust

controllers that can achieve .,-,il:-l .1.ic stability for a class of uncertain time-d, 1 li- 1

nonlinear systems with additive bounded disturbances. The approach described in the

current effort uses a continuous implicit learning [96] based Robust Integral of the Sign of

the Error (RISE) structure [11, 27]. Due to the added benefit of reduced control effort and

improved control performance, an adaptive controller in conjunction with RISE feedback

structure is designed. However, since the time dl 1iv value is not .liv .,i- known, it becomes

challenging to design a delay free adaptive control law. Through the use of a desired

compensation adaptive law (DCAL) based technique and segregating the appropriate

terms in the open loop error system, the dependence of parameter estimate laws on the

time d. 1 .i-, 1 unknown regression matrix is removed. Contrary to previous results, there is

no singularity in the developed controller. A Lyapunov-based stability analysis is provided

that uses an LK functional along with Young's inequality to remove time d. 1 I. terms

and achieves .i-vmptotic tracking.

1.2 Contributions

This dissertation focuses on developing nonlinear controllers for a musculoskeletal

system excited by NMES. The controllers are developed to account for various technical

challenges hampering an effective NMES control performance such as unknown nonlinear

muscle model, muscle fatigue, input and measurement delay. The contributions of

C'!i lpters 3-6 are as follows.

1. C'!I iter 3, Nonlinear Neuromuscular Electrical Stimulation Tracking Control of
a Human Limb: The contribution of this chapter is to illustrate how a recently
developed continuous feedback method called robust integral of signum of the error
(coined as RISE) can be applied for NMES systems. The muscle model developed
in C'!i lpter 2 is rewritten in a form that adheres to RISE-based Lyapunov stability
analysis. Through this error-system development, the continuous RISE controller
is proven (through a Lyapunov-based stability analysis) to yield an .,i- i !,1 l)tic
stability result despite the uncertain nonlinear muscle model and the presence









of additive bounded disturbances (e.g., muscle spasticity, fatigue, changing loads
in functional tasks, and d-.1 i-). The performance of the nonlinear controller is
experimentally verified for a human leg tracking on a leg extension machine by
applying the controller as a voltage potential across external electrodes attached
to the distal-medial and proximal-lateral portion of the quadriceps femoris muscle
group. The RISE controller is implemented by a voltage modulation scheme with a
fixed frequency and a fixed pulse width. Other modulation strategies (e.g., frequency
or pulse-width modulation) could have also been implemented (and applied to other
skeletal muscle groups) without loss of generality. The experiments illustrate the
ability of the controller to enable the shank to track single and multiple period
trajectories with different ranges of motion, and also track desired step changes with
changing loads.
The second result in the chapter focuses on blending NN-based feedforward
technique with RISE based feedback method which was shown to yield .,- ii!! 1 .i i,
tracking in the presence of a nonlinear uncertain muscle model with nonvanishing
additive disturbances. The first result uses feedback and an implicit learning
mechanism to dominate uncertainty and disturbances. Recent results from general
control systems literature [27] indicate that the RISE-based feedback structure
can be augmented with a NN feedforward term to yield .,-,il i l'1 ic tracking for
some classes of systems. Based on these general results, the RISE-based method
is modified with a multi 1 ,-, i t1 NN to develop a new NMES controller for the
uncertain muscle model. The experimental results indicate that the addition of the
NN reduces the root mean squared (RMS) tracking error for similar stimulation
effort when compared to the first method developed in the chapter(RISE method
without the NN feedforward component). Additional experiments were conducted to
depict that the NN-based feedforward technique holds promise in clinical-type tasks.
Specifically, a preliminary sit-to-stand experiment was performed to show controller's
feasibility for any functional task.

2. C'!i lpter 4, Nonlinear Control of NMES: Incorporating Fatigue and Calcium Dy-
namics An open-loop error system for an uncertain nonlinear muscle model is
developed that includes the fatigue and calcium dynamics. A virtual control
input is designed using nonlinear backstepping technique which is composed of a
NN based feedforward signal and an error based feedback signal. The NN based
control structure is exploited not only to feedforward muscle dynamics but also to
approximate the error generated due to parametric uncertainties in the assumed
fatigue model. The actual external control input (applied voltage) is designed based
on the backstepping error. Through this error-system development, the continuous
NN based controller is proven (through a Lyapunov-based stability analysis) to yield
an uniformly ultimately bounded stability result despite the uncertain nonlinear
muscle model and the presence of additive bounded disturbances (e.g., muscle
spasticity, changing loads in functional tasks, and d,-1 i-).

3. C'! i lter 5, Predictor-Based Control for an Uncertain Euler-Lagr gi, System with
Input D. 1.ai; This chapter focuses on the development of a tracking controller for









an uncertain nonlinear Euler-Lagrange system with input delay. The input time
delay is assumed to be a known constant and can be arbitrary large. The dynamics
are assumed to contain parametric uncertainty and additive bounded disturbances.
The first developed controller is based on the assumption that the mass inertia is
known, whereas the second controller is based on the assumption that the mass
inertia is unknown. The key contributions of this effort is the design of a delay
compensating auxiliary signal to obtain a time d. 1 iv free open-loop error system
and the construction of LK functionals to cancel time d, 1 li. d1 terms. The auxiliary
signal leads to the development of a predictor-based controller that contains a finite
integral of past control values. This d, 1 li .1 state to delay free transformation is
analogous to the Artstein model reduction approach, where a similar predictor-based
control is obtained. LK functionals containing finite integrals of control input values
are used in a Lyapunov-based analysis that proves the tracking errors are semi-global
uniformly ultimately bounded. Extensive experiments were performed to show the
controller's better performance in comparison to traditional PID/PD controllers and
robustness to uncertainty in time delay and inertia matrix. Additional experiments
show that the developed controller can be applied to compensate input delay in
NMES.

4. C'! lpter 6, RISE-Based Adaptive Control of an Uncertain Nonlinear System with
Unknown State D. /l; The development in this chapter is motivated by the lack
of continuous robust controllers that can achieve .,-vmptotic stability for a class
of uncertain time-d, 1 ,li .1 nonlinear systems with additive bounded disturbances.
The approach described in the current effort uses a continuous implicit learning
[96] based Robust Integral of the Sign of the Error (RISE) structure [11, 27]. Due
to the added benefit of reduced control effort and improved control performance,
an adaptive controller in conjunction with RISE feedback structure is designed.
However, since the time d. 1 iv value is not ah--bi-i known, it becomes challenging to
design a delay free adaptive control law. Through the use of a desired compensation
adaptive law (DCAL) based technique and segregating the appropriate terms in
the open loop error system, the dependence of parameter estimate laws on the time
d. 1 .i-, 1 unknown regression matrix is removed. Contrary to previous results, there
is no singularity in the developed controller. A Lyapunov-based stability analysis is
provided that uses an LK functional along with Young's inequality to remove time
d, 1 li, '1 terms and achieves .i-i-i!1 il Iic tracking.










CHAPTER 2
MUSCLE ACTIVATION AND LIMB MODEL

The following model development represents the musculoskeletal dynamics during

neuromuscular electrical stimulation performed on human quadriceps muscle. The model

simulates limb dynamics when external voltage is applied on the muscle. The total muscle

knee joint model can be categorized into body segmental dynamics and muscle activation

and contraction dynamics. The muscle activation and contraction dynamics explains the

force generation in the muscle while the body segmental dynamics considers the active

moment and passive joint moments.

The total knee-joint dynamics can be modeled as [6]


M1 + + 11[, + r T.. + =. (2-1)


In (2-1), MI(q) E R denotes the inertial effects of the shank-foot complex about the


Contraction and Activation Dynamics Body Segmental Dynamics





Dy Recruitment Calcium ertial
Curve Dynamics Passive Force Force

Gravitational
Fatie Force
Model





1oltage Input
Controller


Figure 2-1. Muscle activation and limb model. The force generating contraction and
activation dynamics in the muscle is denoted by an unknown nonlinear function
Tr(q, q) E R in the dynamics. The detailed contraction and activation dynamics including
fatigue and calcium dynamics are introduced in C'!h plter 4.









knee-joint, i (q) E R denotes the elastic effects due to joint stiffness, .1.,(q) E R denotes

the gravitational component, i .(q) E R denotes the viscous effects due to damping in the

musculotendon complex [97], rd(t) E R is considered as an unknown bounded disturbance

which represents an unmodeled reflex activation of the muscle (e.g., muscle spasticity)

and other unknown unmodeled phenomena (e.g., dynamic fatigue, electromechanical

d-i-' ), and r(t) E R denotes the torque produced at the knee joint. In the subsequent

development, the unknown disturbance Td(t) is assumed to be bounded and its first and

second time derivatives are assumed to exist and be bounded. These are reasonable

assumptions for typical disturbances such as muscle spasticity, fatigue, and load changes

during functional tasks. For simplicity, the passive damping and elastic force of muscle

and joints are considered together. The inertial and gravitational effects in (2-1) can be

modeled as

M1((t)) = J(t), -,(q(t)) = -mglsin(q(t)), (2-2)

where q(t), q(t), q(t) E R denote the angular position, velocity, and acceleration of the

lower shank about the knee-joint, respectively (see Fig. 2-2), JE R denotes the unknown

inertia of the combined shank and foot, mE R denotes the unknown combined mass of

the shank and foot, E R is the unknown distance between the knee-joint and the lumped

center of mass of the shank and foot, and g E R denotes the gravitational acceleration.

The elastic effects are modeled on the empirical findings by Ferrarin and Pedotti in [97] as


ii. (q) = -k(exp(-k2q(t)))(q(t) k3), (2-3)

where kl, k2, k3 E R are unknown positive coefficients. As shown in [6], the viscous

moment 1.(q) can be modelled as

Vif..((t)) B1 tanh(-B2(t)) B3(t), (2-4)

where B1, B2, and B3 E R are unknown positive constants.




















Figure 2-2. The left image illustrates a person's left leg in a relaxed state. The right image
shows the left leg during stimulation. The angle q(t) is measured with respect to the
vertical line as shown.


The torque produced about the knee is controlled through muscle forces that are

elicited by NMES. For simplicity (and without loss of generality), the development in

this chapter focuses on producing knee torque through muscle tendon forces, denoted by

Fr(t) E I, generated by electrical stimulation of the quadriceps (i.e., antagonistic muscle
forces are not considered). The knee torque is related to the muscle tendon force as

r(t) = ((q(t))FT(t), (2-5)

where ((q(t)) IR denotes a positive moment arm that changes with the extension and

flexion of the leg as shown in studies by [98] and [99]. The tendon force FT(t) in (2-5) is

defined as

F F cos a(q) (2-6)

where a(q(t)) is defined as the pennation angle between the tendon and the muscle. The

pennation angle of human quadriceps muscle changes monotonically during quadriceps

contraction and is a continuously differentiable, positive, monotonic, and bounded function

with a bounded first time derivative [100]. The relationship between muscle force and

applied voltage is denoted by the unknown function q((q, q) E R as


F(t) = (q,j)V(t), (2-7)









where V(t) E R is the voltage applied to the quadriceps muscle by electrical stimulation.

While exact force versus voltage models are debatable and contain parametric uncertainty,

the generally accepted empirical relationship between the applied voltage (or similarly,

current, frequency [101, 102], or pulse width) is well established. The empirical data in

[101] and [102] indicates the function rq(q, q) is a continuously differentiable, non-zero,

positive, monotonic, and bounded function, and its first time derivative is bounded.

The total force generated at the tendon could be considered as the sum of forces

generated by an active element (often denoted by FCE), the tension generated by a passive

elastic element (often denoted by FpE), and forces generated by viscous fluids (often

denoted by FVE). These forces have dynamic characteristics. For example, the passive

element increases with increasing muscle length, and the muscle stiffness has been reported

to change by greater than two orders of magnitude [34] under dynamic contractions. The

muscle model in the chapter considers the total muscle force composed of the sum of

these elements as the function of an unknown nonlinear function q](q, q) and an applied

voltage V(t). The introduction of the unknown nonlinear function rq(q, q) enables the

muscle contraction to be considered under general dynamic conditions in the subsequent

control development. Expressing the muscle contraction forces in this manner enables the

development of a control method that is robust to changes in the forces, because these

effects are included in the uncertain nonlinear muscle model that is incorporated in the

stability analysis. The model developed in (2-1)-(2-7) is used to examine the stability

of the subsequently developed controller, but the controller does not explicitly depend

on these models. The following assumptions are used to facilitate the subsequent control

development and stability analysis.

Assumption 1: The moment arm ((q) is assumed to be a non-zero, positive,

bounded function [98, 99] whose first two time derivatives exist, and based on the

empirical data [101, 102], the function rq(q, q) is assumed to be a non-zero, positive,

and bounded function with a bounded first and second time derivatives.









Assumption 2: The auxiliary non-zero unknown scalar function Q(q, q) E R is

defined as

Q = ( cosa, (2-8)

where the first and second time derivatives of Q(q, q) are assumed to exist and be bounded

(see Assumption 1).

Assumption 3: The unknown disturbance -d(t) is bounded and its first and second

derivatives with respect to time exist and are bounded. Based on Assumptions 1 and 2,

the ratio Td(t)/l((q, q) is also assumed to be bounded and its first and second derivatives

with respect to time exist and are bounded.









CHAPTER 3
NONLINEAR NEUROMUSCULAR ELECTRICAL STIMULATION (NMES)
TRACKING CONTROL OF A HUMAN LIMB

3.1 Introduction

An open-loop error system for a general uncertain nonlinear muscle model is

developed in the chapter by grouping terms in a manner that facilitates the development

of a new continuous feedback method (coined RISE for Robust Integral of the Sign

of the Error in [11, 12])and its extension through combining NN-based feedforward

method. Through this error-system development, the continuous RISE controller and

its modification is proven (through a Lyapunov-based stability analysis) to yield an

.ii-i:!1ll .I ic stability result despite the uncertain nonlinear muscle model and the presence

of additive bounded disturbances (e.g., muscle spasticity, fatigue, changing loads in

functional tasks). The performance of the two nonlinear controllers is experimentally

verified for human leg tracking by applying the controller as a voltage potential across

external electrodes attached to the distal-medial and proximal-lateral portion of the

quadriceps femoris muscle group. The RISE and NN + RISE controllers are implemented

by a voltage modulation scheme with a fixed frequency and a fixed pulse width. Other

modulation strategies (e.g., frequency or pulse-width modulation) could have also been

implemented (and applied to other skeletal muscle groups) without loss of generality.

Third section of the chapter discusses the development of RISE controller for

uncertain nonlinear muscle model. The experiments illustrate the ability of the controller

to enable the leg shank to track single and multiple period trajectories with different

periods and ranges of motion, and also track desired step changes with changing loads.

In fourth section the RISE-based method is modified with a NN to develop a new NMES

controller for the uncertain muscle model. The experimental results indicate that the

addition of the NN reduces the root mean squared (RMS) tracking error for similar

stimulation effort when compared to the first result (RISE method without the NN









feedforward component). A preliminary test was also conducted on a healthy volunteer to

test the capability of the controller to enable the person to perform a sit-to-stand task.

3.2 Control Development

A high-level objective of NMES is to enable a person to achieve some functional

task (i.e., functional electrical stimulation (FES)). Towards this goal, the objective of

the current effort is to develop a NMES controller to produce a knee position trajectory

that will enable a human shank to track a desired trajectory, denoted by qd(t) e R. The

desired trajectory can be any continuous signal (or a simple constant setpoint). In the

subsequent experimental results, the desired trajectories were selected as periodic signals

(for simplicity and without loss of generality) of different frequencies and step functions

with changes in the dynamic load. Although such trajectories may not be truly functional,

trajectory-based movements are necessary for the performance of many FES augmented

tasks (e.g., repetitive stepping during walking). Whether the desired trajectories are based

on limb position, as in the current result, or other information (e.g., desired joint kinetics

or kinematics), the ability to precisely track a desired pattern is fundamental to eliciting

reproducible movement patterns during functional tasks.

To quantify the objective, a position tracking error, denoted by ei(t) E R, is defined

as

ei(t) = qd(t)- q(t), (3-1)

where qd(t) is an a priori trajectory which is designed such that qd(t), qj(t) E ,,where

qd(t) denotes the ithderivative for i = 1, 2, 3, 4. To facilitate the subsequent analysis,
filtered tracking errors, denoted by e2(t) and r(t) E R, are defined as


e2(t) = et) + ae,(t), (3-2)

r(t) = e2(t) + a2e2(t), (3-3)









where ac, a2 E R denote positive constants. The filtered tracking error r(t) is introduced

to facilitate the closed-loop error system development and stability analysis but is not used

in the controller because of a dependence on acceleration measurements.

3.3 Nonlinear NMES Control of a Human Limb via Robust Integral of
Signum of Error (RISE) method

After multiplying (3-3) by J and utilizing the expressions in (2-1) and (2-5)-(3-2),

the following expression can be obtained:


Jr = W QV + rd, (3-4)


where W(eI, e2, t) E R is an auxiliary signal defined as


W = J(qd + a1~i + a262) + i + 1-, + 11. (3-5)


and the continuous, positive, monotonic, and bounded auxiliary function Q(q, t) E R is

defined in (2-8). After multiplying (3-4) by -l (q, t) e R, the following expression is

obtained:

JQr = WQ V + dn, (3-6)

where J(q, t) E R, Trd(q, t) E R, and W(eli,e2,t) E R are defined as

J rd
JQ = TdQ (3-7)



W
WQ = W Jn(qd + ae + a2e2) + + ... + (38)

To facilitate the subsequent stability analysis, the open-loop error system for (3-6) can be

determined as

Jr = r +N- V e2, (3-9)

where N(el, e2, r, t) E R denotes the unmeasurable auxiliary term

1.
N =W + e2 Qr + -dQ(q, t). (3 10)
2









To further facilitate the analysis, another unmeasurable auxiliary term, Nd(qd, qd, qd, qd, t) E
R, is defined as

Nd = (qd)qd + J (qd) d + .(q) + i.. .(qd) + d. .(q) + d (qd, t). (3-11)

After adding and subtracting (3-11) to (3-9), the open-loop error system can be expressed

as
JA -V e2 + d + Nd tJr, (3-12)
2
where the unmeasurable auxiliary term ]N(el, e2, r, t) E R is defined as

N(t) = N Nd. (3-13)

Motivation for expressing the open-loop error system as in (3-12) is given by the

desire to segregate the uncertain nonlinearities and disturbances from the model into
terms that are bounded by state-dependent bounds and terms that are upper bounded

by constants. Specifically, the Mean Value Theorem can be applied to upper bound

N(ei,e2, r, t) by state-dependent terms as


N < p (|| ) | | (3-14)

where z(t) E R3 is defined as

z(t) ^ [eT e r]T, (3-15)

and the bounding function p (|| ||) is a positive, globally invertible, nondecreasing
function. The fact that qd(t), qd(t) E V i = 1,2, 3, 4 can be used to upper bound

Nd(qd, qd, gd, qd, t) as

w||N < ( Nd < (. (3-16)

where (Nd and (Nd c R are known positive constants.









Based on the dynamics given in (2-1)-(2-7), the RISE-based voltage control input

V(t) is designed as

V(t) A (k, + l)e2(t) (k + 1)e2(0) + v(t), (3-17)

where ks E R denotes positive constant adjustable control gain, and v(t) E R is the

generalized solution to

v(t) = (k, + l)a22(t) + 3sgn(e2(t)), v(0) 0, (3-18)

where E R denotes positive constant adjustable control gain, and sgn(-) denotes the

signum function. Although the control input is present in the open-loop error system

in (3-4), an extra derivative is used to develop the open-loop error system in (3-12)

to facilitate the design of the RISE-based controller. Specifically, the time-derivative

of the RISE input in (3-17) looks like a discontinuous sliding mode controller. Sliding

mode control is desirable because it is a method that can be used to reject the additive

bounded disturbances present in the muscle dynamics (e.g., muscle spasticity, load

changes, electromechanical d.1 ,i-,) while still obtaining an .,-i- ii!ll ic stability result. The

disadvantage of a sliding mode controller is that it is discontinuous. By structuring the

open-loop error system as in (3-12), the RISE controller in (3-17) can be implemented as

a continuous controller (i.e., the unique integral of the sign of the error) and still yield an

.,-i-~, iill ic stability result. Without loss of generality, the developed voltage control input

can be implemented through various modulation methods (i.e., voltage, frequency, or pulse

width modulation).

3.3.1 Stability Analysis

Theorem 1. The controller given in (3 17) ensures that all system -.:l,,l- are bounded

under closed-loop operation. The position tracking error is n, gi.il.l I in the sense that


I|le(t)| 0 as t oc, (319)









and the controller ;./,. 1.1 semi-l.1- lrl .',/ ',,;/,I1. .: tracking provided the control gain ks,
introduced in (3 17) is selected suffi.. .: l, '/7./, and/3 is selected according to the

following sufficient condition:

S> (N, (3-20)

where (Nd and (g are introduced in (3-16).
Proof for Theorem 1: Let D C R4 be a domain containing y(t) = 0, where

y(t) e R3+1 is defined as

(t) A [zT (t) I (3-21)

and the auxiliary function P (t) E R is the generalized solution to the differential equation

P (t) = -L (t) P (0) /3 p e2 (0)| e2 (0) Nd (0). (3-22)

The auxiliary function L (t) E R in (3-22) is defined as

L (t) r (Nd (t)- psgn (e)) (3-23)

Provided the sufficient conditions stated in Theorem 1 are satisfied, then P (t) > 0.

Let VL (y, t) : D x [0, oo) -+ R denote a Lipschitz continuous regular positive definite
functional defined as
11
VL (, t) A eTe, + e2e2 + rJr + P, (3-24)
2 2
which satisfy the inequalities


U, (y)< VL (y, t)< U2 (y), (3-25)

provided the sufficient condition introduced Theorem 1 is satisfied, where Ui (y) U2 (y)
R are continuous, positive definite functions. After taking the time derivative of (3-24),

VL (y, t) can be expressed as

1 1
21 (y, t) 1 2eej + Ce262 + Jor+ Jr2 + P. (3-26)
2 2








From (3-2), (3-3), (3-12), (3-22), and (3-23), some of the differential equations
describing the closed-loop system for which the stability analysis is being performed
have discontinuous right-hand sides as

ei = e2 ale1, (3-27a)

r2 = r- a22, (3-27b)

Jr = -Jlr + N + Nd- e2 (k, + 1)r psgn(e2), (3-27c)

P (t) -r (Nd (t)- sgn (e2)) (3-27d)

Let f(y, t) E R4 denote the right hand side of (3-27). Since the subsequent analysis
requires that a solution exists for y = f(y, t), it is important to show the existence of
the solution to (3-27). As described in [103-106], the existence of Filippov's generalized
solution can be established for (3-27). First, note that f(y, t) is continuous except in the
set {(/y,t)|e2 = 0}. From [103-106], an absolute continuous Filippov solution y(t) exists
almost everywhere (a.e.) so that

y K[f](y, t) a.e. (3-28)

Except the points on the discontinuous surface {(y, t)e2 = 0}, the Filippov set-valued
map includes unique solution. Under Filippov's framework, a generalized Lyapunov
stability theory can be used (see [106-109] for further details) to establish strong stability
of the closed-loop system. The generalized time derivative of (3-24) exists a.e., and

VL(y, t) Eae VL (y,t) where
T
VL = vEL(y,t) K 2 1p-P
VL Kr 2 2 iP T

V LTK 1 2 7 P-2'P 1

C 2e 2 r1J 2 jP r2 K \ e, 22
2 2 -'P









where OV is the generalized gradient of V [107], and K[.] is defined as [108, 109]

K[f](y) cof (B(x, ) N),
6>0 N=0

where n denotes the intersection of all sets N of Lebesgue measure zero, co denotes
pN=0
convex closure, and B(x, 6) represents a ball of radius 6 around x. After utilizing (3-2),

(3-3), (3-12), (3-17), (3-18), (3-22), and (3-23)


VL (y,t) C 2ele2-2ace2 +e2r-( '.'+ Jr +rN + d r re2- C (ks+1)r2
2
-prK[sgn(e2) Jr2 rNd(t) + 3rK[sgn(e2)], (3-29)
2

where [108]

K[sgn(e2)] = SGN(e2), (3-30)

such that
1 e2 >0
SGN(e2) [-1,1] 2 e 0 (3-31)

-1 e2 <0
Cancelling common terms and based on the fact that

2ele2< <6l2 + 6111 (3-32)

(3-29) can be written as


VL (y, t) C -(2ai 1)e2 (a2 1)e r2 + r kr2. (3 33)

As shown in (3-29)-(3-33), the unique integral signum term in the RISE controller

is used to compensate for the disturbance terms included in Nd(qd, qd, ld, t', t), provided

the control gain 3 is selected according to (3-20). Using (3-14), the term r(t)NV(ei, 2, r, t),

can be upper bounded by following inequality:

rN < p(| : ) |||| ||r (3 34)









to obtain


VL (y,t) C -min{2ai 1, 1,1} + [p (|| ||) || | ||r|| k, ||r||2]. (3-35)

Completing the squares for the bracketed terms in (3-35) yields


VL (Y,t) C min{2ao -,- t, 2 ,1} 1 +, (3-36)
4k8

The following expression can be obtained from (3-36):


S(y, t)c -U(y), (3-37)

where U (y) is a continuous positive definite function, provided k, is selected sufficiently

large based on the initial conditions of the system. That is, the region of attraction can

be made arbitrarily large to include any initial conditions by increasing the control gain k,

(i.e., a semi-global type of stability result), and hence

c 1||(t)| 2 0 as t oo Vy(0) e S. (3-38)

Based on the definition of z(t) in (3 15), (3-38) can be used to show that


I||e(t) | 0 as t -oo Vy(0) S. (3-39)

3.3.2 Experimental Results

Experiments were performed using the RISE controller given in (3 17). The voltage

controller was implemented through an amplitude modulation scheme composed of a

variable amplitude positive square wave with a fixed pulse width of 100 p sec and fixed

frequency of 30 Hz. The 100 psec pulse width and the 30 Hz stimulation frequency were

chosen a-priori and represent parametric settings that are within the ranges typically

reported during NMES studies. During stimulation at 100 psec pulse widths, human

skeletal muscle response to changes in stimulation amplitude (force-amplitude relationship)

and frequency (force-frequency relationship) are highly predictable and thus deemed









appropriate for use in the present study. The 30 Hz stimulation was selected based on

force-frequency curves [110] which show that as stimulation frequency is increased muscle

force increases to a saturation limit. Higher frequencies can be chosen to generate more

force up to a saturation limit, but muscles tend to fatigue faster at higher frequencies.

The 30 Hz pulse wave yields reduced fatigue in comparison to higher frequencies but

lower frequencies tend to produce rippled knee motion [35, 110]. Therefore stimulation

frequencies in the range of 30-40 Hz is an optimal choice for conducting external electrical

stimulation. The following results indicate that the RISE algorithm was able to minimize

the knee angle error while dynamically tracking a desired trajectory.

3.3.2.1 Testbed and protocol

The tested consists of a custom computer controlled stimulation circuit and a

modified leg extension machine (LEM). The LEM was modified to include optical

encoders. The LEM allows seating adjustments to ensure the rotation of the knee is

about the encoder axis. A 4.5 kg (10 lb.) load was attached to the weight bar of the LEM,

and a mechanical stop was used to prevent hyperextension.

In the experiment, bipolar self-adhesive neuromuscular stimulation electrodes were

placed over the distal-medial and proximal-lateral portion of the quadriceps femoris muscle

group and connected to the custom stimulation circuitry. Prior to participating in the

study, written informed consent was obtained from all the subjects, as approved by the

Institutional Review Board at the University of Florida. Tracking experiments for a two

period desired trajectory were conducted on both legs of five subjects. The subjects

included two healthy females and three healthy males in the age group of 22 to 26 years.

The electrical stimulation responses of healthy subjects have been reported as similar to

paraplegic subjects' responses [16, 22, 39, 111]. Therefore healthy subjects were used in

NMES experiments as substitute for paraplegic patients which were not available. As

described in Section 3.3.2.2, the results were approximately equal across the subjects

(i.e., a standard deviation of 0.53 degrees of Root Mean Squared (RMS) tracking error).









Therefore, additional experiments were conducted on a single subject's leg to illustrate the

applicability of the controller for different conditions.

During the experiments each subject was instructed to relax and to allow the

stimulation to control the limb motion (i.e., the subjects were not supposed to influence

the leg motion voluntarily and were not allowed to observe the desired trajectory).

Varying the time period and range of motion may also help to reduce any possible

trajectory learning and anticipation by a healthy subject. To experimentally examine if

any trajectory learning occurred, four successive tests were conducted on a healthy subject

with a two minute interval between trials. The experiments were conducted for 15 seconds

on a dual period trajectory of 4 and 6 seconds. The resulting RMS errors are given in

Table 3-1. The results in Table 3-1 illustrate that trajectory learning by the subject is not

apparent since the standard deviation between the successive trials is 0.039 degrees.

Trial RMS error ( in deg.)
1 4.35
2 4.28
3 4.26
4 4.29
Table 3-1. Tabulated results indicate that the test subject was not learning the desired
trajectory since the RMS errors are relatively equal for each trial.


3.3.2.2 Results and discussion

The experimental results of five subjects tested for the two period desired trajectory

depicted in Fig. 3-1, are summarized in Table 3-2. In Table 3-2, the maximum steady-state

error is defined as the maximum absolute value of error that occurs after 4 seconds of the

trial. The maximum steady-state errors range from 4.25 to 7.55 degrees with a mean of

6.32 degrees and a standard deviation of 1.18 degrees. The RMS tracking errors range

from 20 to 3.47 with a mean RMS error of 2.75 degrees and a standard deviation of 0.53

degrees. The tracking error results for Subject B and the corresponding output voltages

computed by the RISE method (prior to voltage modulation) are shown in Fig. 3-1. The









results successfully illustrate the ability of the RISE controller to track the desired two


period trajectory.


Subject Leg RMS Max.
Error Steady
State Error
A Left 2.890 7.550
A Right 2.360 7.140
B Left 2.000 5.400
B Right 2.350 6.990
C Left 2.070 4.250
C Right 2.940 4.51
D Left 3.470 7.300
D Right 2.890 6.940
E Left 3.110 6.800
E Right 3.450 6.300
Mean 2.750 6.320
Std. Dev. 0.530 1.180
Table 3-2. Experimental results for two period desired trajectory


To further illustrate the performance of the controller, experiments were also

conducted for trajectories with faster and slower periods and larger ranges of motion.

Specifically, the controller's performance was tested for a desired trajectory with a

constant 2 second period, a constant 6 second period, a triple periodic trajectory

with cycles of 2, 4, and 6 seconds and for a higher range of motion of 65 degrees. As

indicated in Table 3-1, the results for the two period trajectory yielded similar results

for all the subjects. Hence, these additional tests were performed on a single individual

simply to illustrate the capabilities of the controller, with the understanding that some

variations would be apparent when implemented on different individuals. The RMS

tracking errors and maximum steady-state errors are provided in Table 3-3. The RMS

error and the maximum steady state errors are lowest for a constant 6 second period

desired trajectory and higher for faster trajectories and higher range of motion. These

results are an expected outcome since tracking more .-- ressive trajectories generally yields

more error. The triple periodic trajectory consists of a mix of slower and faster period











-60 -60




0 0





40 40
A --5ii-, 5

0 10 20 30 0 10 20 30
Time (se) Time (sec)


35 35






201 20 '
0 10 20 30 0 10 20 30
Time (sec) Time (sec)


Figure 3-1. Top plots: Actual left limb trajectory of a subject (solid line) versus the
desired two periodic trajectory (dashed line) input. (left leg top left plot and right leg
- top right plot). Middle plots: The tracking error (desired angle minus actual angle) of
a subject's leg tracking a two periodic desired trajectory. (left leg middle left plot and
right leg middle right plot). Bottom plots: The computed RISE voltage during knee joint
tracking for the case of two period trajectory (left leg bottom left plot and right leg -
bottom right plot).


trajectories, therefore the RMS and the maximum steady state errors are in between the

respective errors obtained for more .,.-.-ressive 2 second period and higher range of motion

desired trajectories. Figs. 3-2 3-5 depict the errors for the experiments summarized in

Table 3-3.

Additional experiments were also conducted to examine the performance of the

controller in response to step changes and changing loads. Specifically, a desired trajectory
of a step input was commanded with a 10 pound load attached to the LEM. An additional









Trajectory A B
Constant 6 sec. 2.88 6.13
Constant 2 sec. 4.11 10.67
Triple periodic (6, 4, 2) sec. 3.27 7.82
Triple periodic (6, 4, 2) sec with 5.46 12.48
higher range of motion
Table 3-3. Summarized experimental results for multiple, higher frequencies and higher
range of motion. Column (A) indicates RMS error in degrees, and column (B) indicates
maximum steady state error in degrees.


V60

40

20

i-40


Time (s)


0 10 20 30
Time (sec)


Figure 3-2. Top plot: Actual limb trajectory (solid line) versus the desired triple periodic
trajectory (dashed line). Bottom plot: The limb tracking error (desired angle minus actual
angle) of a subject tracking a triple periodic desired trajectory.


10 pound load was added once the limb stabilized after a step down of 15 degrees. The

limb was again commanded to perform a step response to raise the limb back up an

additional 15 degrees with the total load of 20 pounds. The results are shown in Fig. 3-6.

The steady state error was within 1 degree. A maximum error of 3 degrees was observed

when the external load was added. The results give some indication of the controller's













40


0I P
0 10 20 30
Time (s)








0 10 20 30
Time (sec)

Figure 3-3. Top plot: Actual limb trajectory (solid line) versus the desired constant period
(2 sec) trajectory (dashed line). Bottom plot: The limb tracking error (desired angle
minus actual angle) of a subject tracking a constant period (2 sec) desired trajectory.


ability to adapt to changes in load and step inputs and motivate possible future case

studies with neurologically impaired individuals that express muscle spasticity.

For each experiment, the computed voltage input was modulated by a fixed pulse

width of 100 p sec and fixed frequency of 30 Hz. The stimulation frequency was selected

based on subject comfort and to minimize fatigue. During preliminary experiments

with stimulation frequencies of 100 Hz, the subjects fatigued approximately two times

faster than in the current results. The results also indicate that a 100 f sec pulse width

was acceptable, though future studies will investigate higher pulse widths in the range

of 300 350/1 sec which recruit more slow fatiguing motor units [110]. Our previous

preliminary experiments indicated that longer pulse widths (e.g., 1 msec) produced similar

effects as a direct current voltage.










280,.





0

0 10 20 30
Time (s)







S-10 .....
0 10 20 30
Time (sec)

Figure 3-4. Top plot: Actual limb trajectory (solid line) versus the triple periodic desired
trajectory with higher range of motion (dashed line). Bottom plot: The limb tracking
error (desired angle minus actual angle) of a subject tracking a triple periodic desired
trajectory with higher range of motion


The use of the RISE control structure is motivated by its implicit learning characteristics

[96] and its ability to compensate for additive system disturbances and parametric

uncertainties in the system. The advantage of the RISE controller is that it does not

require muscle model knowledge and guarantees .,-i-!,iill1 ic stability of the nonlinear

system. The experimental results indicate that this feedback method may have promise in

some clinical applications.

Although the RISE controller was successfully implemented, the performance of the

controller may be improved by including a feedforward control structure such as neural

networks (a black box function approximation technique) or physiological/phenomenological

muscle models. Since the RISE controller is a high gain feedback controller that yields

.,-i-ii! ,ll. ic performance, adding a feedforward control element may improve transient and
















0
0 10 20 30
Time (s)
10


S 0



0 10 20 30
Time (sec)

Figure 3-5. Top plot: Actual limb trajectory (solid line) versus the desired constant period
(6 sec) trajectory (dashed line). Bottom plot: The limb tracking error (desired angle
minus actual angle) of a subject tracking a constant period (6 sec) desired trajectory.


steady state performance and reduce the overall control effort, thereby reducing muscle

fatigue. Another possible improvement to the controller is to account for fatigue. Fatigue

can be reduced for short durations by selecting optimal stimulation parameters, but

functional electrical stimulation (FES) may require a controller that adapts with fatigue

to yield performance gains for longer time durations. Therefore our future goal will be to

include a fatigue model in the system to enhance the controller performance.

3.3.3 Conclusion

A Lyapunov-based stability analysis indicates that the closed-loop nonlinear control

method yields .i, i!!Ill i tracking for a nonlinear muscle activation and limb dynamics,

even in the presence of additive disturbances. Experiments using external electrodes

on human subjects demonstrated the ability of the RISE controller to enable a limb to

track a desired trajectory composed of varying amplitude and frequency sinusoids, step












0 ----. ......
2o 4 00 .. ....................... ......... ........




0
0 10 20 30
Time (s)






S 1 0 .. . . . .

0 10 20 30
Time (sec)

Figure 3-6. Top plot : Actual limb trajectory (solid line) versus desired step trajectory
(dashed line). The limb is tested for two step inputs. The load is added once the limb
stabilizes ( between 23 and 24 second interval). Bottom plot: The limb tracking error for
step inputs.


changes, and changes in the load. Specifically, the experimental results indicated that

with no muscle model (and only voltage amplitude modulation), the RISE algorithm

could determine the appropriate stimulation voltage for the tracking objective. For the

fastest tested trajectory the maximum steady-state tracking errors were approximately

10 degrees, whereas the maximum steady-state error in slower trajectories were as little

as approximately 4 degrees. An advantage of this controller is that it can be applied

without knowledge of patient specific parameters like limb mass or inertia, limb center

of gravity location, parameters that model passive and elastic force elements. Thus, its

application would not require specific expertise or extensive testing prior to use. The

control development also accounts for unmodeled disturbance (e.g. muscle spasticity) that

are commonly observed in clinical populations. The proposed strategy holds promise for









clinical implementation of the controller as a therapeutic tool to enhance muscle function

during isolated joint movements. However, results have yet to demonstrate functional

movements (e.g. walking) in populations without the ability to voluntarily activate their

muscles. As such, future directions will focus on studies to demonstrate the effectiveness

of the controller under such conditions. Although the trajectories used in the experiments

may not be truly functional, the controller can be applied to any continuous trajectory.

This is clinically relevant because trajectory-based movements are necessary for the

performance of many FES augmented tasks (e.g., repetitive stepping during walking).

Whether the desired trajectories are based on limb position, as in the current result,

or other information (e.g., desired joint kinetics or kinematics), the ability to precisely

track a desired pattern is fundamental to eliciting reproducible movement patterns during

functional tasks. An advantage of the control development is that it allows for inter- as

well as intra-individual variations in trajectory tracking (i.e. task performance) to be

accounted for both within and between sessions (e.g. during rehabilitation training),

thus potentially providing a tool to aid in the future advancement of rehabilitation. A

possible disadvantage of the controller is that high gains are used to achieve the robustness

to disturbances and unmodeled effects. The next section will investigate augmenting

the RISE structure with feedforward control architectures that can accommodate for

disturbances without requiring high gain feedback.

3.4 Modified Neural Network-based Electrical Stimulation for Human Limb
Tracking

NN-based estimation methods are well suited for NMES because the muscle model

contains unstructured nonlinear disturbances as given in (2 1). Due to the universal

approximation property, NN-based estimation methods can be used to represent the

unknown nonlinear muscle model by a three-ii -r NN as [112]


f(x) =WTa(UTx)+ c(x), (3-40)









for some input x(t) E RIN1+. In (3-40), U e R(Ni+1)xN2 and W e R(WN2+)xn are

bounded constant ideal weight matrices for the first-to-second and second-to-third l v. rs

respectively, where N1 is the number of neurons in the input l ..V-r, N2 is the number

of neurons in the hidden lI.-r, and n is the number of neurons in the output l -.,r.

The sigmoid activation function in (3-40) is denoted by a(-) : RN*+1 -- RN2+, and

e(x) : R N1+1 -+ IR is the functional reconstruction error. The additional term "1" in the

input vector x(t) and activation term (-.) allows for thresholds to be included as the first

columns of the weight matrices [112]. Thus, any tuning of W and U then includes tuning

of thresholds. Based on (3-40), the typical three l-v-r NN approximation for f(x) is given

as [112]

f(x)= WTa(UTx), (3-41)

where U e R(N,+1)xN2 and We R( N2+)xn are subsequently designed estimates of the

ideal weight matrices. The estimate mismatch for the ideal weight matrices, denoted by

U(t) e R(N+I1)xN2 and W(t) e R(N2+1)xn, are defined as

S= U U, W W (3-42)

and the mismatch for the hidden-li-v-r output error for a given x(t), denoted by a(x) E

RN2+1, is defined as

a = = a(UTx) a(UTx). (3-43)

The NN estimate has ceratin properties and assumptions that facilitate the subsequent

development.

Property 1: (Taylor Series Approximation) The Taylor series expansion for a(UTy)

for a given y(t) may be written as [112]


a(UTY) = a(^Ty) + a'(UTy)UTy + O(UTy)2, (344)









where o'((UTy) = da(UTy)/d(UTy)UlTy= Ty and O(UTy)2 denotes the higher order terms.
After substituting (3-44) into (3-43) the following expression can be obtained:

a = &Ty + O(UTy)2 (3 45)

where a' = a'(UTy).

Assumption 1: (Boundedness of the Ideal Weights) The ideal weights are assumed

to exist and are bounded by known positive values so that

IlU12 = tr(UTU)= ec(U)TVec(U) < Us, (3-46)



I11II l= tr(WTW)= ec(W)Tvec(W) < WB, (347)

where I||-|F is the Frobenius norm of a matrix, tr(-) is the trace of a matrix. The

ideal weights in a NN are bounded, but knowledge of this bound is a non-standard

assumption in typical NN literature (although this assumption is also used in textbooks

such as [112, 113]). If the ideal weights are constrained to stay within some predefined

threshold, then the function reconstruction error will be larger. Typically, this would

yield a larger ultimate steady-state bound. Yet, in the current result, the mismatch

resulting from limiting the magnitude of the weights is compensated through the RISE

feedback structure (i.e., the RISE structure eliminates the disturbance due to the function

reconstruction error).
3.4.1 Open-Loop Error System

The open-loop tracking error system can be developed by multiplying (3-3) by J and

by utilizing the expressions in (2-1) and (2-5)-(3-2) as

Jr = J(a2eC + ane + d) + + i + i .. QV + Td, (3-48)









where Q(q, q) is defined in (2-8). The dynamics in (3-48) can be rewritten as


JQr =fd + S V + -d (3-49)

where the auxiliary functions fd(qdqd,d qd) E R and S(q, qd, q, qd, qd) E R are defined as


fd = LQ(qd, qd) + J (qd)qd, (3-50)

S = JQ(q)(a2e2+ alel) + JQ(qqd J(qd)qd + LQ(q,q) LQ(qdA, d)

and JQ(q, <) E R, LQ(q, ) E R, and Tdn(q, t) E R are defined as

T J 7Td i + 1[,+i (+ ).
JQ dan La = (3-51)

The expression in (3-50) can be represented by a three-li -r NN as


fdA WTa(UTXd) + C(xd), (3-52)

where Xd(t) E R4 is defined as Xd(t) = [1 qd(t) qd(t) qd(t)]. Based on the assumption that

the desired trajectory is bounded, the following inequalities hold


||(X)|| < ,i b I (Xd)\ < 42 &(X) < eb3, (3-53)

where Cb,, i I'l 3 E R are known positive constants.

3.4.2 Closed-Loop Error System

The control development in this section is motivated by several technical challenges

related to blend the NN feedforward term with the RISE feedback method. One of the

challenges is that the NN structure must be developed in terms if the desired trajectories

to avoid the use of acceleration measurements. Also, while the NN estimates are upper

bounded by constants, the time derivatives of these terms are state dependent, and hence

violate the traditional RISE assumptions. To address this issue, the closed-loop error

system development requires a strategic separation and regrouping of terms. In this

section, the control is designed and the closed-loop error system is presented. Based on the








open-loop error system in (3-49) and the subsequent stability an 1 ,-i- the control torque
input is designed as [27]
V =d+l (3-54)

where fd(t) E R is the three-i V-r NN feedforward estimate designed as

f WT(U^Txd) (3-55)

and p(t) E R is the RISE feedback term designed as [11, 96, 114, 115]

p(t) A (k, + l)e2(t) (k, + l)e2(0) + v(t). (3-56)

The estimates for the NN weights in (3-55) are generated on-line using a projection
algorithm as

W =proj (oa17'T ; U = Proj (2d ( 2 TWe (3-57)
) T)

where F1 e R(N2+1)x(N2+1) and F2 E 4x4 are constant, positive definite, symmetric gain
matrices. In (3-56), k, E IR denotes positive constant adjustable control gain, and v(t) E R
is the generalized solution to

v(t) = (k, + 1)a2e2(t) + j 2sgn(e2(t)), v(0) = 0, (3-58)

where fl E R denotes positive constant adjustable control gain, and sgn(-) denotes the
signum function. The closed-loop tracking error system can be developed by substituting
(3-54) into (3-49) as
Jr = fd + S- p + TdQ, (3-59)

where
fd(Xd) = fd fd. (3-60)









To facilitate subsequent closed loop stability analysis, the time derivative of (3-59) can be

determined as

JQ = -Jr + fd + S i + d. (3-61)

Although the voltage control input V(t) is present in the open loop error system in (3-49),

an additional derivative is taken to facilitate the design of the RISE-based feedback

controller. After substituting the time derivative of (3-60) into (3-61) by using (3-52) and

(3-55), the closed loop system can be expressed as
.T .T
J. =- -Jr + WT' (UTXd)UTX d (- W Tx) WTa'(uTxdu) uTd wU wWT,'(uT.Xd) J Xd

+ (Xd) + S- + dn, (3-62)

where a'(UTx) = d(UTx)/d(UTx) IUTX (T1 After adding and subtracting the terms

WT&'/VTXd + WT&'/VTXd to (3-62), the following expression can be obtained:

JQ =-JQr + WT' VTaXd + WTa'fVT'Xd WT, 'VTXd -WTa'VTXd (3-63)
.T T
+ WTa'UTXd + (Xd) Wa' Xd-- + S i + Tn,

where the notation & (.) is introduced in (3-43). Using the NN weight tuning laws

described in (3-57), the expression in (3-63) can be rewritten as

1.
JQ. = 2-J r + N + N e2 (k, + 1) r psgn(e2), (3-64)

where the unmeasurable auxiliary terms N(e,e2, r, t) and N(W, U, d, t) E R given in

(3-64) are defined as

N(t) = J2r + +2 -- poj (r ,, Pj W 'pTOj (F2 d (rwTC2 Xd

(3-65)

N = NB + Nd. (3-66)








In (3-66), Nd(q, q,xd,x_, t) E R is defined as


Nd WTaUTXd + t(xd) + rd, (3-67)

while NB(W, U, xd,Jd,t) C R is defined as

NB= NB1 + NB,, (3-68)

where NB,(W, U, xd, xd,t) and NB2(W, U, xd, xd,t) E R are defined as

B, = -WTa'T 'X WTUUiTXd, (3-69)

and
NB, = W 'TU + WTl'UTXd. (3-70)

Motivation for the definitions in (3-65)-(3-67) are based on the need to segregate terms
that are bounded by state-dependent bounds and terms that are upper bounded by
constants for the development of the NN weight update laws and the subsequent stability
analysis. The auxiliary term in (3-68) is further segregated to develop gain conditions
in the stability analysis. Based on the segregation of terms in (3-65), the Mean Value
Theorem can be applied to upper bound N(el,e2, r, t) as

N < p (| |) | (3-71)

where z(t) E R3 is defined as
z(t) ^ [e e rT]T, (3-72)

and the bounding function p (||l |) E R is a positive globally invertible nondecreasing
function. Based on Assumption 3 in ('!i plter 2, (3-46), (3-47), (3-53), and (3-68)-(3-70),
the following inequalities can be developed [27]:

INd <_ 1 NBl < (2 Nd <(3 (373)

NB








where ( E R ,(i = 1, 2, ...5) are known positive constants.
3.4.3 Stability Analysis

Theorem 2. The composite NN and RISE controller given in (3-54)-(3-58) ensures that

all system -:,,it..,l are bounded under closed-loop operation and that the position tracking
error is ,, i.,l., .l in the sense that


||ei(t)| 0 as t oo, (3-74)

within some set S containing the initial conditions of the system, provided the control
gains in (3 56) and (3 58) are selected suff .'. i.l, '/1/; 7
Proof for Theorem 2: Let D C R5 be a domain containing y(t) = 0, where

y(t) E R5 is defined as

y(t) (3-75)

where the auxiliary function Q (t) E R is defined as


Q (t) A tr (IW W + 2tr (UT (376)
F22 J) 2(3-76
and P (t) E R is the generalized solution to the differential equation

P (t) = -L (t) P (0) 1 pl2 (0) e2 (0) N (0). (3-77)

Since Fi and F2 in (3-76) are constant, symmetric, and positive definite matrices, and

c2 > 0, it is straightforward that Q (t) > 0. The auxiliary function L (t) E R in (3-77) is
defined as

L (t) A r (NB (t) + Nd(t) Assgn (e2)) + C2NB (t) 2e2(t)2, (3 78)

where si, /32 E R introduced in (3-58) and (3-78) respectively, are positive constants
chosen according to the following sufficient conditions

1 > (1 + (2 + C3 + 4, /2 > C5, (3-79)
Q2 Q2









where (i E R ,(i = 1, 2,..., 5) are known positive constants introduced in (3-73). Provided
the sufficient conditions in (3-79) are satisfied, then P (t) > 0.
Let VL (y, t) : D x [0, oo) -- R denote a Lipschitz continuous regular positive definite
functional defined as

VL (y, t) 2 + t 2 + P+JQ, (3-80)

which satisfies the inequalities

S(y) < VL (Y, t) < U2 (), (381)

provided the sufficient conditions in (3-79) are satisfied, where Ui (y) U2 (y) c R are
continuous, positive definite functions defined as

U, (y) = A y112, U2 (y) A2 IX12, (3-82)

where A1, A2 E R are known positive functions or constants. From (3-2), (3-3), (3-64),
(3-77), (3-78), and after taking the time derivative of (3-76), some of the differential
equations describing the closed-loop system for which the stability analysis is being
performed have discontinuous right-hand sides as

e = e2 ale1, (3-83a)

e2 = r a2e2, (3-83b)

J,, = Jr + 1N + N e2 (k + 1) r- sgn(e2), (3-83c)
2
P (t) -r (NB, (t) + Nd(t) isgn (e2))- 2NB, (t) + 22(t)2, (3-83d)

Q (t) = (at 2 WT 1-/) + (trau2 T2 ) (3-83e)

Let f(y, t) e R5 denote the right hand side of (3-83). f(y, t) is continuous except in the
set {(y,t)le2 = 0}. From [103-106], an absolute continuous Filippov solution y(t) exists
almost everywhere (a.e.) so that

E K[f](y,t) a.e.









The generalized time derivative of (3-80) exists a.e., and VL(y, t) e VL (y, t) where

a 1s
VL (y, t) = 10n K elr +2 (7k +)- IQ-Q g(3-84)
T
= VLT 2 7 1P-P 1Q- ,

r 1 K t
C 2e, e2 rJQ 2P 2 2Q 1K 2 _P 1

For more details of the notations used in 3-83 to 3-84 and discussion, see Section 3.3.1.
After utilizing (3-2), (3-3), (3-64), (3-77), (3-78), the expression in 3-84 can be rewritten
as


VL (y, t) C 2eie2 2ale + e2r ae2 + .]^ + rN + rN e2 (ks + 1) r2 prK[sgn(eC2
2
jr2 rNB, rNd(t) + prK[sgn(eC2) BeMN(t + 0 + tr a2 W 1W

+tr (a2 fTF-u (3-85)

Using (3-57), (3-66), (3-68), (3-70), cancelling common terms, and based on the fact
that
2ele2< IJ 11 2+ 211 2

(3-85) can be written as

VL (y,t) C -(2ai l)e (a2 32 l)e| r2 + rN k,2. (386)

As shown in (3-85)-(3-86), the unique integral signum term in the RISE controller
is used to compensate for the disturbance terms included in Nd(qd, qd, qd, 9d, t) and

NB, (W, U, xd, Xd, t), provided the control gain Pl and f2 are selected according to
(3-79). Further the term NB2(W, U, xd, xd, t) is partially rejected by the unique integral
signum term and partially cancelled by adaptive update law. Using (3-71), the term









rT(t)N(ei, e2, r, t), can be upper bounded by following inequality:


rN < p (11 11) 11 1 r ,


to obtain


VL (y,t) c -min {2ai a 32 ,2 } '+ [p (11 : ) | : I||I r kr2].

Completing the squares for the bracketed terms in (3-87) yields

p2 ( : ) :
VL (y, t) C min {2ai 1, a2 -- 2 ,1} II 1 +
4kg


(3-87)


(3-88)


The following expression can be obtained from (3-88):


VL (y, t) C -U(y),


(3-89)


where U (y) = c ||:|, for some positive constant c E R, is a continuous positive

semi-definite function that is defined on the following domain:

D y Rhe I I, < d a s d a )

where A3 A min{2i 1, a2 /2 1, 1}. Let S C D denote a set defined as follows:


S y(t)c 2 (y(t)) < A 1 (2VA3k)2)


(3-90)


where S C D is introduced in Theorem 2. The region of attraction in (3-90) can be made
arbitrarily large to include any initial conditions by increasing the control gain k, (i.e., a

semi-global type of stability result), and hence


cll :(t)112


as t oo


Vy(O) e S.


(3-91)


Based on the definition of z(t) in (3-72), (3-91) can be used to show that


Vy(O) E S.


(3-92)


as t oo


Ilel(t II -









3.4.4 Experimental Results

Results are provided in this section that examine the performance of the controller

given in (3-54)-(3-58) in experiments with volunteer subjects. These results were

compared with the previous results in [116] that used the RISE feedback structure without

the NN feedforward term. The NMES controller was implemented as an amplitude

modulated voltage composed of a positive rectangular pulse with a fixed width of 400

p sec and fixed frequency of 30 Hz. The a priori chosen stimulation parameters are within

the ranges typically reported during NMES studies [110, 116]. Without loss of generality,

the controller is applicable to different stimulation protocols (i.e., voltage, frequency, or

pulse width modulation). The following results indicate that the developed controller

(henceforth denoted as NN+RISE) was able to minimize the knee angle error while

dynamically tracking a desired trajectory.

3.4.4.1 Testbed and protocol

The tested consists of a custom computer controlled stimulation circuit and a

modified leg extension machine (LEM). The LEM was modified to include optical

encoders. The LEM allows seating adjustments to ensure the rotation of the knee is

about the encoder axis. A 4.5 kg (10 lb.) load was attached to the weight bar of the LEM

and a mechanical stop was used to prevent hyperextension.

The objective in one set of experiments was to enable the knee and lower leg to

follow an angular trajectory, whereas, the objective of a second set of experiments was to

regulate the knee and lower leg to a constant desired setpoint. An additional preliminary

test was also performed to test the capability of the controller for a sit-to-stand task. For

each set of experiments, bipolar self-adhesive neuromuscular stimulation electrodes were

placed over the distal-medial and proximal-lateral portion of the quadriceps femoris muscle

group of volunteers and connected to custom stimulation circuitry. The experiments

were conducted on non-impaired male and female subjects (as in our previous study in

[116]) with age ranges of 20 to 35 years, with written informed consent as approved by









the Institutional Review Board at the University of Florida. The electrical stimulation

responses of non-impaired subjects have been reported as similar to paraplegic subjects'

responses [16, 22, 39, 111]. The volunteers were instructed to relax as much as possible

and to allow the stimulation to control the limb motion (i.e., the subject was not supposed

to influence the leg motion voluntarily and was not allowed to see the desired trajectory).

The NN+RISE controller was implemented with a three input 1i,-vr neurons,

twenty-five hidden 1i,-vr neurons, and one output 1-v r neuron. The neural network

weights were estimated on-line according to the adaptive algorithm in (3-57). For each

experiment, the computed voltage input was modulated by a fixed pulse width of 400

p sec and fixed frequency of 30 Hz. The stimulation frequency was selected based on

subject comfort and to minimize fatigue. Nine subjects (8 males, 1 female) were included

in the study. The study was conducted for different types of desired trajectories including:

a 1.5 second periodic trajectory, a dual periodic trajectory (4-6 second), and a step

trajectory. For the 1.5 second periodic trajectory, controllers were implemented on both

legs of four subjects, while the rest of the tests were performed on only one leg of the

other three subjects since they were not available for further testing. Three subjects (1

male, 1 female (both legs); 1 male (one leg)) were asked to volunteer for the dual periodic

desired trajectory tests while regulation tests were performed on one of the legs of two

subjects. Each subject participated in one trial per criteria (e.g., one result was obtained

in a session for a given desired trajectory). For each session, a pre-trial test was performed

on each volunteer to find the appropriate initial voltage for the controller to reduce the

initial transient error. After the pre-trial test, the RISE controller was implemented on

each subject for a thirty second duration and its performance was recorded. A rest period

of five minutes was provided before the NN+RISE controller was implemented for an

additional thirty second duration.

















Time [sec]
15


0 5
0 .................


0 5 10 15 20 25 30
Time [sec]
30
I25









middle plot shows the tracking error (desired angle minus actual angle). The maximum
steady state error obtained is 5.95 (at 20.7 sec.). The bottom plot shows the computed
15
0 5 10 15 20 25 30
Time [sec]


Figure 3-7. The top plot shows the actual limb trajectory (solid line) obtained from the
RISE controller versus the desired 1.5 second period desired trajectory (dashed line). The
middle plot shows the tracking error (desired angle minus actual angle). The maximum
steady state error obtained is 5.95' (at 20.7 sec.). The bottom plot shows the computed
RISE voltage. The maximum steady state voltage obtained is 28.1 V (at 21.47 sec.).


3.4.4.2 Results and discussion

The knee/lower limb tracking results for a representative subject with stimulation

from the RISE and the NN+RISE controllers are shown in Figs. 3-7-3-8 and are

summarized in Table 3-4. In Table 3-4, the maximum steady state voltage (SSV) and

maximum steady state error (SSE) are defined as the computed voltage and absolute value

of error respectively, that occur after 1.5 seconds of the trial. Paired one tailed t-tests

(across the subject group) were performed with a level of significance set at a = 0.05. The

results indicate that the developed controller demonstrates the ability of the knee angle

to track a desired trajectory with a mean (for eleven tests) RMS error of 2.92 degrees

with a mean maximum steady state error of 7.01 degrees. Combining the NN with the

RISE feedback structure in [116] yields (statistically significant) reduced mean RMS error
















0 5 10 15 20 25 30
Time [sec]
15
7 1 0 ... ..:. .. .. .. .. . . . ..

0 .

0 5 10 15 20 25 30
Time [sec]
30


r 25


0 5 10 15 20 25 30
Time [sec]


Figure 3-8. The top plot shows the actual limb trajectory (solid line) obtained from the
NN+RISE controller versus the desired 1.5 second period desired trajectory (dashed
line). The middle plot shows the tracking error (desired angle minus actual angle). The
maximum steady state error obtained is 4.240 (at 28.6 sec.). The bottom plot shows the
computed NN+RISE voltage. The maximum steady state voltage obtained is 26.95 V (at
29.1 sec.).


for approximately the same input stimulus. The maximum steady state voltages for the

RISE and NN+RISE controllers revealed no statistical differences. To illustrate that the

performance of NN+RISE controller (in comparison to the RISE controller alone) can

be more significant for different desired trajectories, both controllers were implemented

on three subjects (2 male, 1 female) with the control objective to track a dual periodic

(4 6 second) desired trajectory with a higher range of motion. The stimulation results

from the RISE and the NN+RISE controllers are shown in Figs. 3-9 and 3-10 and are

summarized in Table 3-5. In Table 3-5, the maximum SSV and SSE were observed after

4 seconds of the trial. The results illustrate NN+RISE controller yields reduced mean

RMS error (across the group) and reduced mean maximum SSE (across the group) for










Subject Leg RMS Error Max SSE RMS Voltage [V] Max SSV [V]
RISE NNR RISE NNR RISE NNR RISE NNR
A Left 3.590 2.920 12.420 7.590 22.91 23.98 29.5 31
A Right 2.600 2.630 5.740 6.510 27.70 25.40 32.95 31.5
B Left 2.470 2.230 5.950 4.240 22.41 22.81 28.1 26.95
B Right 2.830 2.740 6.280 6.760 25.10 23.03 29.8 30.5
C Left 3.180 2.460 8.1 6.170 41.35 40.14 48.9 44.8
C Right 2.970 3.010 6.90 9.630 36.32 35.15 46.4 42.3
D Left 3.230 3.71 6.040 5.860 25.25 28.24 30 34.1
D Right 3.530 2.960 8.80 7.580 13.62 14.95 24.2 23.4
E Left 3.920 3.260 11.150 7.920 30.89 31.46 45 40.5
F Left 3.380 2.830 7.990 6.41 26.15 28.13 31.8 34.1
G Left 3.520 3.320 8.20 8.450 41.59 43.44 49.8 50
Mean 3.200 *2.920 7.960 7.010 28.48 28.79 36.04 35.38
Std. Dev. 0.450 0.410 2.180 1.440 8.49 8.29 9.44 8.08
p-value 0.02 0.08 0.28 0.22

Table 3-4. Summarized experimental results and P values of one tailed paired T-test for
a 1.5 second period desired trajectory. indicates statistical difference. NNR stands for
NN+RISE controller.


approximately the same input stimulus. Paired one tailed t-tests (across the subject

group) were performed with a level of significance set at a = 0.05. The results show that

the difference in mean RMS error and mean maximum SSE were statistically significant.

The P value for the mean RMS error (0.00043) and mean maximum SSE (0.0033) t-test

obtained in the case of dual periodic trajectory is smaller when compared to the P values

(0.02 and 0.08, respectively) obtained for the 1.5 second trajectory. This difference

indicates the increased role of the NN for slower trajectories (where the adaptation gains

can be increased).

As in [117], additional experiments were also conducted to examine the performance

of the NN+RISE controller in response to step changes and changing loads. Specifically,

a desired trajectory of a step input was commanded with a 10 pound load attached to

the LEM. An additional 10 pound load was added once the limb stabilized at 15 degrees.

The limb was again commanded to perform a step response to raise the limb back up an




















Time [sec]







0 5 10 15 20 25 30
Time [sec]
30



0


0 5 10 15 20 25 30
Time [sec]



Figure 3-9. The top plot shows the actual limb trajectory (solid line) obtained from the
RISE controller versus the dual periodic desired trajectory (dashed line). The middle plot
shows the tracking error (desired angle minus actual angle). The maximum steady state
error obtained is 6.560 (at 21 sec). The bottom plot shows the computed RISE volatge.
The maximum steady state voltage obtained is 29.67 V (at 26.7 sec.).


additional 15 degrees with the total load of 20 pounds. The results from a representative

subject using NN+RISE controller are shown in Fig. 3-11. The experimental results

for the step response and load addition are given in Table 3-6. The results give some

indication of the controller's ability to adapt to changes in load and step inputs and

motivate possible future case studies.

Experiments were also performed to test the NN+RISE controller for a sit-to-stand

task. These tests were conducted on a l. ,ril!:v individual initially seated on a chair (see

Fig. 3-12). The knee angle was measured using a goniometer (manufactured by Biometrics


















0 5 10 15 20 25 30
Time [sec]


10 5 1 1 2 2 30


-5
Ti0 ....

0 5 10 15 20 25 30
Time [sec]

3 0 '"





0 5 10 15 20 25 30
Time [sec]


Figure 3-10. The top plot shows the actual limb trajectory (solid line) obtained from
the NN+RISE controller versus the dual periodic desired trajectory (dashed line). The
middle plot shows the tracking error (desired angle minus actual angle). The maximum
steady state error obtained is 4.57 (at 10.5 sec.). The bottom plot shows the computed
NN+RISE volatge. The maximum steady state voltage obtained is 29.68 V (at 26.9 sec.


Ltd.) attached to both sides of the subject's knee, where the initial knee angle is set

to zero (sitting position). The goniometer was interfaced with the custom computer

controlled stimulation circuit via an angle display unit (ADU301). The objective was to

control the angular knee trajectory that resulted in the volunteer rising from a seated

position, with a final desired angle of 900 (standing position). The error, voltage, and

desired versus actual knee angle plots are shown in Fig. 3-13. The RMS error and voltage

during this experiment were obtained as 2.92 and 26.88 V, respectively. The final steady

state error reached within -0.50, the maximum transient error was observed as 8.23,










Subject Leg RMS Error Max SSE RMS Voltage [V] Max SSV [V]
RISE NNR RISE NNR RISE NNR RISE NNR
A Left 2.350 1.850 6.120 4.300 29.08 29.19 34.10 34.09
A Right 1.730 1.260 4.490 3.90 30.00 29.67 35.75 34.62
B Left 3.520 2.620 6.450 5.640 37.09 36.34 44.04 43.47
B Right 3.390 2.890 6.530 6.000 37.88 38.57 45.30 46.19
C Right 3.840 2.820 6.560 4.570 23.99 24.09 29.67 29.68
Mean 2.970 *2.290 6.030 *4.880 31.61 31.57 37.77 37.61
Std. Dev. 0.890 0.710 0.880 0.900 5.84 5.85 6.69 6.93
p-value 0.00043 0.0033 0.43 0.29

Table 3-5. Summarized experimental results and P values of one tailed paired T-test for
dual periodic (4-6 second) desired trajectory. indicates statistical difference. NNR stands
for NN+RISE controller.

Subject Leg Max. SSE Max. Tran- Max. Error Max. SSV (af-
(after step sient Error (during dis- ter step input)
input) turbance) [Volts]
A Left 0.70 9.50 2.80 42.2
B Right 0.60 9.520 2.00 19.2
Table 3-6. Experimental results for step response and changing loads


and the maximum voltage was obtained as 35.1 V. The significance of these tests is to

depict the applicability of the controller on clinical tasks such as sit to stand maneuvers.

Although the experiments were conducted on a healthy individual, these preliminary

results show that the controller holds promise to provide satisfactory performance on

patients in a clinical-type scenario.

The NN+RISE structure is motivated by the desire to blend a NN-based feedforward

method with a continuous feedback RISE structure to obtain .i-, i!ill, i ic limb tracking

despite an uncertain nonlinear muscle response. The ability of the neural networks to

learn uncertain and unknown muscle dynamics is complemented by the ability of RISE to

compensate for additive system disturbances (hyperactive somatosensory reflexes that may

be present in impaired individuals) and NN approximation error. Although the NN+RISE

controller was successfully implemented and compared to RISE controller in the present

work, the performance of the controller may be further improved in efforts to reduce the

























20 5


10 U ....
0 5 10 15 20 25 30

3Time [sec]

0 5 10 15 20 25 30
Time [sec]


Figure 3-11. Experimental plots for step change and load addition obtained from
NN+RISE controller. Top plot shows actual limb trajectory (solid line) versus desired step
trajectory (dashed line). The load is added once the limb stabilizes (between 13-15 second
interval). After load addition the limb is tested for the step input. Middle plot shows the
limb tracking error obtained during the experiment. Bottom plot shows computed voltage
for the experiment.

effects of muscle fatigue in future studies. Fatigue can be reduced for short durations by
selecting optimal stimulation parameters, but functional electrical stimulation (FES) may
require a controller that adapts with fatigue to yield performance gains for longer time
durations. Therefore our future goal will be to include a fatigue model and incorporating
calcium dynamics in the muscle dynamics to enhance the controller performance.



























Figure 3-12. Initial sitting position during sit-to-stand experiments. The knee-angle was
measured using a goniometer attached around the knee-axis of the subject's leg.


3.4.5 Limitations

The results illustrate the added value of including a NN feedforward component

in comparison to only using the RISE feedback structure in [116]. However, several

limitations exist in the experimental study. The contribution from the NN component

was observed to increase but the RISE contribution did not decline proportionally. A

possible reason for this observation is that the 1.5 second period desired trajectory has

a large desired acceleration qd(t), which is an input to the NN that can lead to large

voltage swings during the transient stage. To reduce large voltage variants during the

transient due to qd(t), the update law gains are reduced in comparison to gains that could

be employ, ,1 during less .. .-essive trajectories. The experimental results with slower

trajectories (dual periodic 4-6 second period) illustrate that the NN component can pl i,

a larger role depending on the trajectory. Specifically, the dual periodic trajectory results

indicate that the RMS error obtained with the NN+RISE controller is lower than the

RMS error obtained with the RISE controller with a lower P value (0.00043) compared to

the P value (0.02) obtained with the 1.5 second period trajectory.












I I |
... .. .. .. .



0 -'
0 1 2 3 4
Time [sec]

S10,,
to




0 1 2 3 4


3 5 ... . ... ... .

S25


0 1 2 3 4
Time [sec]


Figure 3-13. The top plot shows the actual leg angle trajectory (solid line) versus desired
trajectory (dotted line) obtained during the standing experiment. The middle plot shows
the error obtained during the experiment. The bottom plot shows the voltage produced
during the experiment.

Since a trajectory for a specific functional task was not provided, the desired
trajectory used in the first set of experiments was simply selected as a continuous sinusoid
with a constant 1.5 second period. The desired trajectory was arbitrarily selected, but
the period of the sinusoid is inspired by a typical walking gait trajectory. As the work
transitions to applications where a specific functional trajectory is generated, the control









results should directly translate. Furthermore, some clinical goals may be better expressed

as a desired force profile rather than a desired limb trajectory. The results from this work

could be directly applied to these cases by altering the control objective and open-loop

error system, but the form of the control method (i.e., NN+RISE) would remain intact.

An analysis of RMS errors during extension and flexion phase of the leg movements

across different subjects, trajectories (1.5 second and dual periodic), and both controllers

showed that the mean RMS error is more when leg is moving upwards (extension phase)

compared to periods when leg is moving downwards (flexion phase). A t-test analysis

showed that the results are statistically significant with p values of 0.00013 and 0.0014

obtained from RISE and NN+RISE controllers, respectively. The mean RMS errors during

extension phase for RISE and NN+RISE controllers were 3.49 and 2.680, respectively

while mean RMS errors during flexion phase for RISE and NN+RISE controllers were

2.960 and 2.420, respectively. Summarized RMS errors for both phases are shown in

Table 3-7. An increased error during extension phase can be attributed to higher control

effort required during extension. The performance during the extension phase can also

be ...: i ivated by increased time delay and muscle fatigue due to the requirement for

higher muscle force compared to the flexion phase. This analysis indicates a possible need

for separate control strategies during extension and flexion phase of the leg movement.

Particularly, future efforts will investigate a hybrid control approach for each phase of

motion.

Currently the experiments were performed on non-impaired persons. In future studies

with impaired individuals, our untested hypothesis is that the added value of the NN

feedforward component will be even more pronounced (and that the controller will remain

stable) as disturbances due to more rapid fatigue and more sensitive somatosensory

reflexes may be present in impaired individuals. To delay the onset of fatigue, different

researchers have proposed different stimulation strategies [32, 33, 118] such as choosing

different stimulation patterns and parameters. The NMES controller in this study was










Subject Leg Trajectory RMS Error (RISE) RMS Error (NN+RISE)
Extension Flexion Extension Flexion
A Left Dual period 4.350 2.41 3.300 1.680
A Right Dual period 3.980 2.680 3.390 2.280
B Left Dual period 2.740 1.860 1.770 1.920
B Right Dual period 1.780 1.690 1.350 1.170
C Right Dual period 4.220 3.430 3.270 2.280
D Left 1.5 second 2.870 2.000 2.540 1.880
D Right 1.5 second 3.21 2.380 3.070 2.380
E Left 1.5 second 3.870 3.300 3.300 2.490
E Right 1.5 second 2.560 2.650 2.340 2.880
F Left 1.5 second 3.81 2.51 4.000 3.400
F Right 1.5 second 3.590 3.47 2.960 2.960
G Left 1.5 second 3.930 2.180 2.860 1.970
G Right 1.5 second 2.980 2.950 2.820 3.190
H Left 1.5 second 4.180 2.700 3.920 2.580
I Left 1.5 second 3.970 2.660 3.110 2.51
J Right 1.5 second 3.790 4.050 3.380 3.130
Mean 3.490 2.680 2.960 2.420
p-value 0.00013 0.0014

Table 3-7. The table shows the RMS errors during extension and flexion phase of the
leg movement across different subjects, trajectories (1.5 second and dual periodic), and
controllers (RISE/NN+RISE). The results show that the mean RMS error is more during
the extension phase than during the flexion phase.


implemented using constant pulse width amplitude modulation of the voltage. However,

the controller can be implemented using other modulation schemes such as pulse width

and frequency modulation without any implications on the stability analysis, but the

effects of using frequency modulation or varying pulse trains (e.g. a pulse train containing

doublets) remain to be investigated clinically.

3.4.6 Conclusion

A Lyapunov-based stability analysis indicates that the developed closed-loop

nonlinear NMES control method yields .,-vmptotic tracking for a unknown nonlinear

muscle activation and limb dynamics, even in the presence of uncertain additive

disturbances. Experiments using external electrodes on non-impaired volunteers









demonstrated the ability of the NN+RISE controller to enable the knee and lower leg

to track a desired trajectory composed of sinusoids, step changes, and changes in the load.

Statistical analysis of the experimental results indicates that the NN+RISE algorithm

yields reduced RMS tracking error when compared to the RISE controller for statistically

insignificant differences in voltage input. A preliminary experiment (a sit-to-stand task)

to test the controller for a clinical-type functional task showed a promising control

performance. These experiments -i-i-. -1 that future efforts can be made to test the

performance on patients with movement disorders. Specifically, experiments should be

conducted for functional tasks such as walking and sit-to-stand maneuvers.









CHAPTER 4
NONLINEAR CONTROL OF NMES: INCORPORATING FATIGUE AND CALCIUM
DYNAMICS

4.1 Introduction

The focus of this chapter is to address muscle fatigue by incorporating an uncertain

fatigue model (i.e., the model developed in [35]) in the NMES controller. The contribution

of the method is that only best guess estimates of patient specific fatigue time constants

and natural frequency of calcium dynamics are required and the mismatch between the

estimated parameters and actual parameters is included in a stability analysis. The

fatigue model is defined as a function of a normalized muscle activation variable. The

normalized muscle activation variable denotes the calcium (Ca2+ ion) dynamics which

act as an intermediate variable between contractile machinery and external stimulus.

The calcium dynamics are modeled as a first order differential equation based on [6] and

[39]. An open-loop error system for an uncertain nonlinear muscle model is developed

that includes the fatigue and calcium dynamics. A virtual control input is designed using

nonlinear backstepping technique which is composed of a NN based feedforward signal

and an error based feedback signal. The NN based control structure is exploited not

only to feedforward muscle dynamics but also to approximate the error generated due to

parametric uncertainties in the assumed fatigue model. The actual external control input

(applied voltage) is designed based on the backstepping error. Through this error-system

development, the continuous NN based controller is proven (through a Lyapunov-based

stability analysis) to yield an uniformly ultimately bounded stability result despite the

uncertain nonlinear muscle model and the presence of additive bounded disturbances (e.g.,

muscle spasticity, changing loads in functional tasks, and d-.1-).

4.2 Muscle Activation and Limb Model

The musculoskeletal model given in C(i Ipter 2 is modified to consider calcium and

fatigue dynamics during neuromuscular electrical stimulation. The additional dynamics









of calcium ions and muscle fatigue are incorporated in the contraction and activation

dynamics while the body segmental dynamics remains the same as provided in C'! lpter 2.

The torque produced about the knee is generated through muscle forces that are

elicited by NMES. The active moment generating force at the knee joint is the tendon

force Fr(t) c R defined as [119]

FT = F cos a, (4-1)

where a(q(t)) E R is defined as the pennation angle between the tendon and the muscle,

where q(t), q(t) E R denote the angular position and velocity of the lower shank about

the knee-joint, respectively (see Fig. 2-2). The pennation angle of the human quadriceps

muscle changes monotonically during quadriceps contraction and is a continuously

differentiable, positive, monotonic, and bounded function with a bounded first time

derivative [100]. The muscle force F(t) E R in (4-1) is defined as [36]

F = Fm, T2P(x)x, (4-2)

where F, E R is the maximum isometric force generated by the muscle. The uncertain

nonlinear functions 1i(q), ry2(q, q) E R in (4-2) are force-length and force-velocity

relationships, respectively, defined as [36, 120, 121]


(q) exp ( b 1)2) (4 3)



h2(q, q) = arctan(c (q, q) + C3) + C4 (4 4)

where b, l(q) e R in (4-3) denote the unknown shape factor and the normalized length

with respect to the optimal muscle length, respectively, and v(q, q) E R is an unknown

non-negative normalized velocity with respect to the maximal contraction velocity of the

muscle, and ci, c2, C3, C4 are unknown, bounded, positive constants.

Assumption: The force-velocity relationship 92 is lower bounded by a known

constant E,. The lower bound on the force-velocity relationship is practical in the sense









that rT2(q, ) = 0 (i.e., no force output) only occurs when the muscle shortening velocity (a

concentric contraction) is at the maximum rate.

The definitions in (4-3) and (4-4) are not directly used in the control development.

Instead, the structure of the relationships in (4-3) and (4-4) is used to conclude that r1q(q)

and q2(q, q) are continuously differentiable, non-zero, positive, monotonic, and bounded

functions, with bounded first time derivatives. The muscle force in (4-2) is coupled to the

actual external voltage control input V(t) E R through an intermediate normalized muscle

activation variable x(t) E R. The muscle activation variable is governed by following

differential equation [34, 119]


2x = -wx + wsat[V(t)l, (4-5)


where w E R is the constant natural frequency of the calcium dynamics. The function

sat[V(t)]E R (i.e., recruitment curve) is denoted by a piecewise linear function as

0 V < Vmi,

sat[V(t)] = V-Vmin Vmin < V < Vmax (46)
Vmax Vmin
1 V > Vmax,

where Vmin E R is the minimum voltage required to generate noticeable movement or force

production in a muscle, and Vmax E R is the voltage of the muscle at which no considerable

increase in force or movement is observed. Based on (4-5) and (4-6), a linear differential

inequality can be developed to show that x(t) E [0, 1]. Muscle fatigue is included in (4-2)

through the invertible, positive, bounded fatigue function p(x) E R that is generated from

the first order differential equation [35, 36]

1 1
=7 ( -+min P)x + (4-7)
If T,

where pmin is the unknown minimum fatigue constant of the muscle, and Tf, T, are

unknown time constants for fatigue and recovery in the muscle, respectively.









Active muscle Netactive
force force






Uncertain
Fatigue Model

Figure 4-1. An uncertain fatigue model is incorporated in the control design to address
muscle fatigue. Best guess estimates are used for unknown model parameters.

4.3 Control Development
The objective is to develop a NMES controller to produce a knee torque trajectory
that will enable a human shank to track a desired trajectory, denoted by qd(t) E R, despite
the uncertain fatigue effects and coupled muscle force and calcium dynamics. Without
loss of generality, the developed controller is applicable to different stimulation protocols
(i.e., voltage, frequency, or pulse width modulation). To quantify the objective, a position
tracking error, denoted by e(t) E R, is defined as

e(t) qd(t) q(t), (4-8)

where qd(t) is an a priori trajectory which is designed such that qd(t), qj(t) E L,, where

q) (t) denotes the ithderivative for i = 1, 2, 3,4. To facilitate the subsequent analysis, a
filtered tracking error, denoted by r(t), is defined as

r(t) e(t) + ae(t), (4-9)

where a E R denotes a positive constant.









4.3.1 Open-Loop Error System

The open-loop tracking error system can be developed by taking the time derivative

of (4-9), multiplying the resulting expression by J, and then utilizing the expressions in

(4-1), (4-2), (2-1), (2-5) and (4-8) as

Ji = J(ae + gd) + Vi + 1., + iV .. + rd pcx, (4-10)

where the auxiliary function p(q, q) E R is defined as

p = cos(a)FmlT7i72 (4 11)

After multiplying (4-10) by p-l(q, q) E R, the following expression is obtained:

Jpi = Jp(ae + id) + Lp + Tdp ox, (4-12)

where J(q, t), Tdp(q, t), Lp(q, q) E R are defined as


Jp = -lJ, Tdp = p-Td,

LP p- 1(. + 3, + f ..).

Property 3: Based on the assumptions and properties (in Section 4.2), p(q, q) is

continuously differentiable, positive, monotonic, and bounded. Also the function p- (q, q)

is bounded. The first time derivatives of p(q, q) and p- (q, q) exist and are bounded. The

inertia function Jp is positive definite and can be upper and lower bounded as

ai I12 < 7TJ7 < a2 2 V7 (4 13)

where a,, a2 E R are some known positive constants. Also using the boundedness of

p(q,q), P(q, ), P-(q,)

Jp < \Tdp < (4-14)

where ij, R E R are some known positive constants.









Based on (4-7) a positive estimate p(x) is generated as


1 1
(7min ) ( + -(1- (- (4-15)
Tf T,
1 > (0) > 0,

where Tf, T E R denote constant best guess estimates of the time constants Tf and T,

respectively, (mjin E R is a non zero positive constant, and x(t) E R is the estimated

normalized muscle activation variable which is generated based on (4-5) as

2x = -iw + wsat[V(t)], (4-16)


where w c R denotes the constant best guess estimate of natural frequency of calcium

dynamics w. The estimated function ('(x) is upper bounded by a positive constant p E R.

Specifically, p can be determined as

T
S= (0) + + 1 + mn. (4-17)
Tf
The algorithm used in (4-15) ensures that (p(x) remains strictly positive. Based on (4-6)

and (4-16), a linear differential inequality can be developed to show that x(t) c [0, 1].

To facilitate the control development, the terms (p(x)x +ip(x)x + p(x)x are added and

subtracted to (4-12) to yield


Jp = S + Tdp ejTr -- p pe 'px, (4-18)

where the auxiliary function S(q, q, qd, e, r, x) E R is defined as

S Jp(qd + oae) + L,(q, q) + 2r + e yx (4-19)

and the error functions pr(x,.), 9((x), x(t) E R are denoted as


G(x) = (x') (x'), (4-20)

e(x, X) = (x) ( ), (4-21)









Sx x. (4-22)

Since ip(x) and p(x) are bounded functions, the error function y(t) can be upper bounded

as

I e < (4-23)

where R E R is some known positive constant. The auxiliary function S(q, q, qd, e, r, ') can

be represented by a three-i i'r NN as

S WTo(UTy) + c(y), (4-24)

where y(t) E R7 is defined as


y( ) [1 q(t) d() t) (t) r(t) x(t) (4-25)

and c(y) is a functional reconstruction error that is bounded by a constant as


I y) < 6. (4-26)

4.3.2 Closed-Loop Error System

Since a direct control input does not appear in the open-loop system in (4-18), a

backstepping-based approach is used to inject a virtual control input Xd(t) E R (i.e.,

desired calcium dynamics) as

1
Jpt = S + Tdp -- X )Pe C jr e CX+ + CXd CXd. (4-27)

Based on (4-27), the virtual control input is designed as a three 1-v-r NN feedforward

term plus a feedback term as

Xd + -1(S + kr), (4-28)

where k, E R denotes a positive constant adjustable control gain. The feedforward NN

component in (4-28), denoted by S(t) E R is generated as

S = iTao(UT y). (4-29)









The estimates for the NN weights in (4-29) are generated on-line using projection
algorithm as [27]

W = proj(Fl6rT'), U = proj(F2y(oTWr)T), (4-30)

where F1 e R(N2+1)x (N2+1) and F2 cE R(Ni+1)x(Ni+1) are constant, positive definite,
symmetric gain matrices. The closed-loop tracking error system can be developed by

substituting (4-28) into (4-27) as

1
J1i = 2-jr e + S + dp x x kr ., (431)

where S(y) E R is defined as

S(y) =S -S, (4-32)

and e,(t) E R is the backstepping error defined as

eC, = Xd. (4 33)

The closed loop system can be expressed as


jPr = 2- r e + WTa (UTy) IWT oa(Ty) + C(y) + rdp x cx kr ;-. ... (4-34)

After adding and subtracting the terms WT1- + WTT to (4-34), the following expression
can be obtained:

Jp, = 2-jr e + WTo + + + eWT) + WT + (Y) p r x kc r k per (4-35)

where the notations o (.) and a (.) are introduced in (3-43). The Taylor series approximation

described in (3-44) and (3-45) can now be used to rewrite (4-35) as

Jpr = 2- r e + N + TIa + WT'-Ty kr ,., (4-36)
2 ,,.








where cr'(UTy) = do(UTy)/d(UTy)ulTy- UTy. The unmeasurable auxiliary term N(W, U, y, p-l,t)
E R is defined as

N V= 'U + Wy + WO(y) + (y) + dp (4 37)

Based on (4-14), (4-23), (4-26), (4-30), the fact that x(t), x(t) e [0,1], and the
assumption that desired trajectories are bounded, the following inequality can be
developed [122]:

INI I ( + (2 1:11, (438)

where ( e R, (i = 1,2) are known positive constants and z e R2 is defined as

z [ r]T. (4-39)

4.3.3 Backstepping Error System
To facilitate the subsequent stability analysis, the time derivative of the backstepping
error (4-33) can be determined by using (4-16) as

e --x + -sat[V(t)]- Xd. (4-40)
2 2

Based on (4-6) and (4-40), and assumption that control input remains below the
saturation voltage Vmax, the control input (Voltage input) V(t) E R is designed as

V(t) = (Vmax Vmin) (- d + c-x + r kex) + Vmin, (4-41)

where k E R denotes a positive constant adjustable control gain. Substituting (4-41) into
(4-40), yields
e = cr ke. (4-42)

4.4 Stability Analysis
Theorem 3. The controller given in (4 -.') and (4-41) ensures that all system .:-,il. are
bounded under closed-loop operation and that the position tracking error is ,, ,l.rla. in the









sense that


|e(t)| < co exp(-cit) +C2, (4-43)

provided the control gains a, k, introduced in (4-9), (449), (4-50) are selected according

to the following sufficient condition:

min(a, ks,) > (2, (4-44)

where co, 1, C2 E R denote positive constants, and (2 is a known positive constant

introduced in (4 38).

Proof: Let VL (t) E R denote a continuously differentiable, non negative, radially
unbounded function defined as

1T 1T 1 1T
VL(t) A e2e + r Jr + e e, + -trW F ) + ttrUF J). (4-45)
2 2 2 2 2

By using (4-13) and typical NN properties [112], VL (t) can be upper and lower bounded

as

A1 X2 VL (t) < A2 IX2 + 0, (446)

where A1,A2, 0 E R are known positive constants, and X(t) e R3 is defined as
T
X(t) A Z(t) eX(t) (447)

Taking the time derivative of (4-45), utilizing (4-9), (4-36), (4-42), and canceling similar

terms yields

VL = -eae + rTN rTkr + rTWT + rTWT'y eTke, r T(j Jp)r

tr(WTF1 W) tr(T F2 ). (4-48)

Using (4-14) and (4-38), the expression in (4-48) can be upper bounded as

VL < -ae2 k1r2 + (2 III Irl + [Irl (i ksr2] ke2 + rTWTcr + rTWTr'OTy tr(WWTFi )

tr(UT F2 1), (4-49)









where k,,k k,2 E R are positive constant gains that satisfy


k, = k,l + k,,. (4-50)

Completing the squares for the bracketed term in (4-49) and using the update laws in
(4-30) yields

VL < -[min(al, k,,) 2] III ke + 2 (4-51)
4k,"
The inequality in (4-46) can be used to rewrite (4-51) as

VL< VL + (4-52)
A2

where E IR is a positive constant defined as

E= + 0, (4-53)
4k,2 A2

and p E R is defined as

/ = min[(min(ao, k,,) (2), k]. (4-54)

The linear differential inequality in (4-52) can be solved as


VL(t)< V()e^ +E C (4-55)

Provided the sufficient condition in (4-44) is satisfied, the expressions in (4-45) and (4-55)

indicate that e(t), r(t), ex(t), W(t), U(t) e ,. Given that e(t), r(t), qd(t), qd(t) E oo,

(4-8) and (4-9) indicate that q(t), q(t) e L,. Since W(t), U(t) e L, (3-42) and
Assumption 1 (3.4) can be used to conclude that W(t), U(t) e /,. Based on (4-5), it

can be shown that x(t) e [0, 1]. Given that qd(t), e(t), r(t), q(t), q(t), x(t) e o, the NN
input vector y(t) E L, from (4-25). Since ex(t), x(t) e /,, (4-33) can be used to show

that Xd(t) e oo. Given that r(t), W(t), U(t), Xd(t) e L, (4-28) and (4-29) indicate that

S(t), 1(t) e C. Since e(t), r(t), W(t), W(t), U(t),ex(t) (t) e L, (4-36) and (4-38)
indicate that r(t) e L,. As r(t), y(t), W(t) e L,, the update laws W(t), U(t) e L,.

Since ((t), L(t) E o, it can be shown that p(t) e o,. Given that the (t), ~-l(t), i(t),










r(t), W(t), U(t), W(t), U(t) e L,, it can be shown that Xd(t) e ,. Because o(t), Xd(t),

r(t), x(t), e,(t) c L, it can be concluded that the voltage control input V(t) is bounded.

4.5 Simulations

Simulations are performed to illustrate the performance of the controller. The model

parameters were chosen from [6, 36, 123]. The RISE and the proposed controller are tested

for two different desired trajectories: 1) slow trajectory with 6 second period, 2) fast

trajectory with 2 second period.


1



0 10 20 30 40
.. 5 0 0 -.. .. ... ...


0
0 10 20 30 40
00



0 10 20 30 40
5 0 | ----------------




Time [sec.]

Figure 4-2. Top plot shows the knee angle error for a 6 second period trajectory using
the proposed controller. Middle plot shows the pulsewidth computed by the proposed
controller. Bottom plot shows the actual leg angle (dashed line) vs desired trajectory
(solid line).


From the results shown in Figs. 4-2-4-8, it is clear that the proposed controller tracks

both time varying desired trajectories better than the RISE controller. Figs. 4-4 and 4-5

illustrate the performance of the RISE controller when implemented on muscle dynamics

without including the fatigue dynamics. The steady state error from the RISE controller

is between 80 for desired trajectory with period 6 seconds. The steady state error in the

case of RISE controller increases to 140 when faster trajectory with period 2 seconds

is used. Fig. 4-6 depicts that the control performance degrades later in time when RISE















-2
0 10 20 30 40
1500
1 0 0 0................. ................
S 500- .-

0 10 20 30 40
-_50



o0
0 10 20 30 40
Time [sec.]

Figure 4-3. Top plot shows the knee angle error for a 2 second period trajectory using
the proposed controller. Middle plot shows the pulsewidth computed by the proposed
controller. Bottom plot shows the actual leg angle (dashed line) vs desired trajectory
(solid line).


controller is implemented on muscle dynamics with fatigue model included. The proposed

controller was implemented on the complete muscle dynamics that included the fatigue

dynamics. Figs. 4-2, 4-3 and 4-7 show that the steady state error in the case of proposed

controller remains within 0.50 for both slow and fast trajectories. Fig. 4-8 shows how the

fatigue variable evolves with time as a deceasing input gain. The proposed controller is

able to compensate for the decreasing control gain, and the performance does not degrade

over time as shown in Fig. 4-7.

4.6 Conclusion

A NN based nonlinear control algorithm is developed to elicit non-isometric

contractions of the human quadriceps muscle via NMES. The primary objective of the

developed method is to incorporate an uncertain muscle fatigue model and unknown

calcium dynamics in the nonlinear muscle dynamics. The unknown muscle model and

the parametric uncertainties in the fatigue model are approximated by the NN structure

through an estimate of the calcium dynamics. A Lyapunov based stability analysis is














-20
0 10 20 30 40
1000

500 -.. ........ .-. ..... -..... ... .... ... -........ ....
0
0 10 20 30 40
-750

/ oy/


0 10 20 30 40
Time [sec.]

Figure 4-4. Top plot shows the knee angle error for a 6 second period trajectory using
the RISE controller. Middle plot shows the pulsewidth computed by the RISE controller.
Bottom plot shows the actual leg angle (dashed line) vs desired trajectory (solid line).


performed to prove uniformly ultimately bounded result in the presence of bounded

disturbances (e.g muscle spasticity), parametric uncertainties. Simulation results clearly

illustrate that the proposed controller performs better in terms of reduced error in

comparison to the RISE controller. However, the performance of the controller on

volunteers or patients remains to be seen. The controller's dependence on acceleration

and mathematical fatigue and calcium models hinder its implementation on volunteers.

The mathematical calcium and fatigue models were incorporated due to the fact that the

measurement of actual fatigue state and calcium variable is difficult. Future efforts can

be made to incorporate an observer-based design in the controller in order to estimate the

fatigue and calcium states.














4U


0 2 0.... : .... ... ..... .... ....

0 10 20 30 40
2000


1000

-10 10 20 30 40
0 10 20 30 40
50 ,

50


0 10 20 30 40
Time [sec.]

Figure 4-5. Top plot shows the knee angle error for a 2 second period trajectory using
the RISE controller. Middle plot shows the pulsewidth computed by the RISE controller.
Bottom plot shows the actual leg angle (dashed line) vs desired trajectory (solid line).






201, ,
20



-20 --------
60 70 80 90 100
S2000

1001


0 10 20 30 40
50




60 70 80 90 100
Time [sec.]

Figure 4-6. RISE controller with fatigue in the dynamics: Top plot shows the knee angle
error for a 6 second period trajectory using the RISE controller. Middle plot shows the
pulsewidth computed by the RISE controller. Bottom plot shows the actual leg angle
(dashed line) vs desired trajectory (solid line).















.J !

0


60 70 80 90 100

S 5 0 0 .. .. ... .. .. .. .. .. .
500


0 10 20 30 40
-7 50.

0


60 70 80 90 100
Time [sec.]

Figure 4-7. Performance of the proposed controller: Top plot shows the knee angle error
for a 6 second period trajectory using the proposed controller. Middle plot shows the
pulsewidth computed by the proposed controller. Bottom plot shows the actual leg angle
(dashed line) vs desired trajectory (solid line).











0.98

0.96

S0.94

u 0 .9 2 . .. . . .

0 .9 .. .. .

0 .8 8 .. ... .... ..

0.86
0 20 40 60 80 100
Time [sec.]

Figure 4-8. Fatigue variable









CHAPTER 5
PREDICTOR-BASED CONTROL FOR AN UNCERTAIN EULER-LAGRANGE
SYSTEM WITH INPUT DELAY

5.1 Introduction

This chapter focuses on the development of tracking controllers for an uncertain

nonlinear Euler-Lagrange system with input delay. The input time delay is assumed to

be a known constant and can be arbitrarily large. The dynamics are assumed to contain

parametric uncertainty and additive bounded disturbances. The first developed controller

is based on the assumption that the inertia matrix is known. The known inertia case is

provided to illustrate how a proportional integral (PID) controller can be augmented to

compensate for input delay. The second controller removes the assumption that inertia

matrix is known, and different design/analysis efforts are used to yield a PD controller

with an augmented predictor component. The key contributions of this effort is the design

of a delay compensating auxiliary signal to obtain a time delay free open-loop error system

and the construction of LK functionals to cancel the time d. 1 i-, .1 terms. The auxiliary

signal leads to the development of a predictor-based controller that contains a finite

integral of past control values. This d, 1 i, .1 state to delay free transformation is analogous

to the Artstein model reduction approach, where a similar predictor-based control is

obtained. LK functionals containing finite integrals of control input values are used in

a Lyapunov-based analysis that proves the tracking errors are semi-global uniformly

ultimately bounded. Experimental results are obtained for a two-link direct drive robot.

The results illustrate the robustness and added value of the developed predictor-based

controllers.

The primary motive of this research is to develop and implement a controller that

compensates for electromechanical delay (EMD) in NMES. The last section of the chapter

focuses on characterizing EMD during NMES. Experiments results obtained from

I. i,11!:r volunteers are provided which describe the effect of stimulation parameters on the

EMD during NMES. Finally, a PD controller with an augmented predictor component









is implemented on the healthy volunteers. Experiments show that the controller can be

applied to compensate EMD in NMES. A comparison with the traditional PD controller

shows that the PD controller with delay compensation provides a better performance.

5.2 Dynamic Model and Properties

Consider the following input d, 1 i. I1 Euler-Lagrange dynamics


M(q)q + Vm(q, q)q + G(q) + F(q) + d(t) = u(t r). (5-1)

In (5-1), M(q) E denotes a generalized inertia matrix, Vm(q, q) E T. denotes a

generalized centripetal-Coriolis matrix, G(q) E R" denotes a generalized gravity vector,

F(q) E PR denotes generalized friction, d(t) E R" denotes an exogenous disturbance (e.g.,
unmodeled effects), u(t r) E R" represents the generalized d. 1 ,i .1 input control vector,

where r E R is a constant time delay, and q(t), q(t), q(t) E R" denote the generalized

states. The subsequent development is based on the assumptions that q(t) and q(t) are

measurable, Vm(q, q), G(q), F(q), d(t) are unknown, the time d,1 iv constant Tr E R is

known1 and the control input vector u(t) and its past values (i.e., u(t 0) V 0 E [0 r])

are measurable. For the controller developed in Section 5.3.2, M(q) is assumed to be

known to illustrate the development of a PID-like controller. In Section 5.3.3, this

assumption is removed and a PD-like controller is developed. Throughout the paper, a

time dependent d. 1 li-,- function is denoted as x(t r) (or as x,) and a time dependent

function (without time delay) is denoted as x(t) (or as x). The following assumptions are

used in the subsequent development.



1 Experimental results (where the time d 1 iv is artificially injected in a desired manner)
illustrate the performance of the developed controllers when the time delay has as much as
10lOi' error between the assumed and actual delay.









Assumption 1: The inertia matrix M(q) is symmetric, positive definite, and satisfies

the following inequality V (t) E R" :

mi |1112 < TM < m2 ,1112, (5-2)

where mi, m2 e R+ are known constants and I||-| denotes the standard Euclidean norm.

Assumption 2: The desired trajectory qd(t) is designed such that qd(t), q) (t) E oo,

where qd)(t) denotes the 1th time derivative for i = 1,2,3.

Assumption 3: If q(t), q(t) E L,, then M(q), V,(q, q), G(q), and F(q) are bounded.

Moreover, if q(t), q(t), q(t) e L, then the first time derivatives of M(q), Vm,(q, ), G(q),

F(q) exist and are bounded. The infinity norm of M(q) and its inverse can be upper
bounded as

||-1(q)1|l < (1 (--(q) < 2, (5-3)

where (1, (2 e R+ are known constants.

Assumption 4: The nonlinear disturbance term and its first time derivative are

bounded, i.e., d(t), d(t) c L,.

5.3 Control Development

5.3.1 Objective

The objective is to develop a controller that will enable the input d, 1 i 1 system in

(5-1) to track a desired trajectory, denoted by qd(t) E R". To quantify the objective, a

position tracking error, denoted by ei(t) E R", is defined as


el = qd(t) q(t). (5-4)

5.3.2 Control development given a Known Inertia Matrix

To facilitate the subsequent analysis, a filtered tracking error, denoted by e2(t) E R",

is defined as

e2 e1 + iei, (5-5)









where acl Rc + denotes a constant. To reduce the input d. 1 i .1 system in (5-1) to an

input delay free system, an auxiliary signal denoted by r(t) E R", is also defined as

r 2 + a262 + M-1(q)(u(t r) u(t)), (5-6)

where a2 E R+ denotes a constant. The auxiliary signal r(t) is only introduced to

facilitate the subsequent analysis, and is not used in the control design since the

expression in (5-6) depends on the unmeasurable generalized state q(t).

After multiplying (5-6) by M(q) and utilizing the expressions in (5-1), (5-4), and

(5-5), the transformed open-loop tracking error system can be expressed in an input delay

free form as


M(q)r = M(q)qd + Vm(q, q)q + G(q) + F(q) + aM(q)e, + a2M(q)e2 + d u(t). (5-7)

Based on (5-7) and the subsequent stability analysis, the control input u(t) E R" is

designed as

u- ka (2 + 0 a22(0) + M-(0O)(u(O T) u(O))dO) ka2(0), (5-8)

where k, E R+ is a known constant that can be expanded as


k = k, + k,2 + 1, (5-9)

to facilitate the subsequent stability analysis, where k,,, ka, E R+ are known constants.

The controller u(t) in (5-8) is a proportional integral derivative (PID) controller modified

by a predictor like feedback term for time delay compensation. Although the control input

u(t) is present in the open loop error system in (5-7), an additional derivative is taken to

facilitate the subsequent stability analysis. The time derivative of (5-7) can be expressed

as

M(q)r --M(q)r + N + d- kr, (5-10)
2









where N(el, e2, r, t) CE R is an auxiliary term defined as


1
N = ()r + M(q)q ++ (q)qqd+V,(q,q)q+V(q,q)q + (q)+F(q) (5-11)
2
+ (aci + a2) M(q)r al2M(q)e2 a M(q)ei a M(q)e2 + O iMV(q)

+a2M(q)e2 (ac + a2) (U, ) M(q)ei,

and (5-6) is used to write the time derivative of (5-8) as


u = kar.

After adding and subtracting the auxiliary function Nd(qd, qd, ld, "d, t) E R" defined as

Nd M(qd)qd) + qd)9d + ) + (qd, qd)qd + K((qd, gd) + dG (qd) + F(qd),


to (5-10), the following expression is obtained:


1 .
M(q)i = --M(q)r + N + S e2 kar,
2


(5-12)


where the auxiliary functions N(ex,e2, r, t) CE R and S(qd, qd, d, q d, t) E R" are defined as


N N- Nd+e2,


S = Nd + d.


(5-13)


Some terms in the closed-loop dynamics in (5-12) are segregated into auxiliary terms

in (5-13) because of differences in how the terms can be upper bounded. For example,

Assumptions 2, 3 and 4, can be used to upper bound S(qd, qd, qd, 4d, t) as


(5-14)


where E1 E R+ is a known constant and the Mean Value Theorem can be used to upper

bound N(e, e2, r, t) as


N < pi(II | ) 1 5 1 ,


(5-15)









where z e -. is defined as

z = e eT rT eT (5-16)

and the bounding function pl (||l |) E R is a known positive globally invertible

nondecreasing function. In (5-16), e, e R" is defined as


ez = u UT (0)d,
t-T

based on the Leibnitz-Newton formula.

Theorem 4. The controller given in (5-8) ensures semi-ill..1' l/// ;,,'.. il,,,;, ;,ll.:i,,i,;/. 1

bounded (SUUB) tracking in the sense that


|ei(t)|| < coexp(-cit) + C2, (5-17)

where co, C1, C2 C R+ denote constants, provided the control gains ac, a2, and ka introduced

in (5-5), (5-6), and (5-8), ,' ./.. /.:,. /;, are selected according to the following sufficient

conditions:
1 (722 1
c > -, a2 > 1 + k< w2 > 27, (5-18)



Proof: Let y(t) c D C L be defined as
T
y(t) e ej rT (5-19)

where Q(t) E R is defined as [45, 76]


Q j It(0) 12 dO ds, (5-20)
9-7

where w E R+ is a known constant. A positive definite Lyapunov functional candidate

V (y, t) : D x [0 oo) R is defined as


V (y, t) A ee + i e22 + 2 rT (q)r + Q, (5-21)
2 2









and satisfies the following inequalities


A 112 < V < A2 ll 2, (5-22)

where Ai, A2 E R+ are known constants defined as

1 1
A1 = min[ml,1], A2 = max[ 2, 1], (5-23)
2 2

where m, and m2 are defined in (5-2).

After utilizing (5-5), (5-6), and (5-12) and cancelling the similar terms, the time

derivative of (5-21) is

V 2e]e2-2aeT e1-a 2e 2- karTr+eCTM-l(q)e +rTS+rTN+wT 112I2-_ IWJ (0)2 d0,
t-T
(5-24)

where the Leibniz integral rule was applied to determine the time derivative of Q(t) in

(5-20) (see the Appendix 7.2). The expression in (5-24) can be upper bounded by using

(5-3), (5-14) and (5-15) as

V < -(2a, 1) ||el12 (a2 1) 162 2 k I|r||2 + 6211 ||e1 (5-25)

t-
wT .112 +1 rI+Il+(1) 1r_ W t(O) 2d0.

The following term in (5-25) can be upper bounded by using Young's inequality:


2 1 2 I 2 2112 + 11 2, (5-26)

where 7 e R+ is a known constant. Further, by using the Cauchy Schwarz inequality, the

following term in (5-26) can be upper bounded as


(527)








Adding and subtracting Jft_ Iit0)l 2 dO in (5 25) yields

V < -(2ai 1) Ile 112 (2 1) li2112 k rIIr22 + 2I11 1ell + IIl12 (5-28)
T T
|1 rI pi(||ll / (lll) 11H II|- r f ) ) it(O) l2 dO 1 t(0) 2d0.
E- 7 1 J-T 7 2T

Utilizing (5-9) and the bounds given in (5-26) and (5-27), the inequality in (5-28) can be
upper bounded as

222 2
V < -(2ai 1) ||el2 a 2 1 2) 2 ( ... 1.) 22 27) 2
4 ) 11

+Pl(ll l) ll| ||r|| ka ||r 2 k., |r||2 IIt(O) l2d0. (5-29)

After completing the squares, the inequality in (5-29) can be upper bounded as

'T I lt (0) 1 2 1 2 1
V < -1 | j (0) ll2 d0l- (5-30)
7 4k ai 4k,,2
where p E R+ is defined as

1 = min (a2- 1 2 ), (2ai 1), (1 ..1 2 .
4 ,2- .


Since
/ t(
t-" Y
the expression in (5
V_<-
V < -

Using the definition
expressed as


it|(0)112d0 ds < S sup [ (0 \ 112 ( d0] j (0)11 2d0,
s [t,t-r] Js t-
30) can be rewritten as

p )1 2 t t F 2
0,1 4 ) 11 2 I T j(0) 2 dO + (5-31)
4k, 4 /1 )Jt-T UJs 44ka2

of z(t) in (5-16) and y(t) in (5-19), the expression in (5-31) can be


V < -< |_ ||Il 2 3 (ll l) lllC 2 k+ 1 (5-32)
/4 k('l 4kai2 (


where Pl(|| I|) R+ is defined as

/i mmin (i


4kll II, y21








By further utilizing (5-22), the inequality in (5-32) can be upper bounded as

S< V +-. (5-33)
A2 4k,,

Consider a set S defined as
S,4, ,< < -
^{z(t)R4 c RI


In S, Pl(|| :|) can be lower bounded by a constant 61 E IR+ as

61 < 0(ll: Il). (5-35)

Based on (5-35), the linear differential equation in (5-33) can be solved as

LiE2 1 t
V(y, t) < V(0)e -^ + e- ^ (5-36)
4k,,26, Ik II

provided I|||| < p1 (2 /3ika/ ) From (5-36), if z(0) E S then ka can be chosen according
to the sufficient conditions in (5-18) (i.e. a semi-global result) to yield the result in (5-17).
Based on definition of y(t), it can be concluded that el(t), e2(t), r(t) E L in S. Given
that el(t), e(t) qd(t), d(t) E in S, (5-4) and (5-5) indicate that q(t), q(t) E L, in
S. Since r(t), e2(t), q(t), (t), qd(t), qd(t) c L in S, and u(t) u(t 7) ft, it(0)dO =

k, t, r(O)dO (by Leibnitz-Newton formula)e LC, in S, then (5-6) and Assumption 3
indicate that q(t) E L, in S. Given that r(t), e2(t), q(t), t(t), qd(t) qd(t) E L" in S, (5-7)
and Assumptions 3 and 4 indicate that u(t) E L in S.
5.3.3 Control development with an Unknown Inertia Matrix
To facilitate the subsequent control design and stability analysis for the uncertain
inertia problem, the auxiliary signal, e2(t) E IR" is redefined as

e2(t) -e + Cae B u(O)dO, (5-37)
Jt-T









where a E R+ is a known constant, and B c. is a known symmetric, positive definite

constant gain matrix that satisfies the following inequality


1|BI |< b (5-38)

where b E R+ is a known constant. To facilitate the subsequent stability analysis, the error

between B and M-l(q) is defined by


TI(q) B M-(q),


(5-39)


where rI(q) E -.


satisfies the following inequality


II(q)|ll < T,


where rl e R+ denotes a known constant. The open-loop tracking error system can

be developed by multiplying the time derivative of (5-37) by M(q) and utilizing the

expressions in (5-1), (5-4), and (5-39) to obtain


M(q)e2 M(q)d + V(q, q)q + G(q) + F() + d + aM(q) u(t) M(q) [u u,]. (5-41)

Based on (5-41) and the subsequent stability analysis, the control input u(t) E R" is

designed as


U = kbe2,


(5-42)


where kb E R+ is a known control gain that can be expanded as


kb = kb, + kb, + kb3,


(5-43)


to facilitate the subsequent analysis, where kb,, kb2, and kb3 E R+ are known constants.

After adding and subtracting the auxiliary term Nd(qd, qd, id, t) e R" defined as

Nd = M (qd)qd + Vm(qd, qd) qd + G(qd) + F (qd) ,


(5-40)









and using (5-37) and (5-42), the expression in (5-41) can be rewritten as


M(q)e2 = M(q)e2 + N + S e1 kbe2 -1 (q)r [e2 eC2, (5-44)

where the auxiliary terms N(ei,e2,t), N(ei,e2,t), S(qdqd, d, t) E R" are defined as

N = N Nd, s =Nd + d, (5-45)

1.
N -lM(q)e2+M(q)qd+Vm(q, )q+G(q)+F(q)+aM(q)e2-a2M(q)ei+ei+aM(q)B u(O)dO,

where N(ei, e2, t) and S(qd, qdd, d, t) can be upper bounded as


N< P2(|I) II, S < < (5-46)

In (5-46), E2 E R+ is a known constant, the bounding function p2 ( II) E I is a positive

globally invertible nondecreasing function, and z E is defined as

z = eT eT C (5-47)

where ez e R' is defined as

e, u(O)dO.
t-T
Theorem 5. The controller given in (5-42) ensures SUUB tracking in the sense that


I||e(t)l| < coexp(-elt) + 2, (5-48)

where co, C1, C2 CE ]+ denote constants, provided the control gains a and kb introduced in

(5-37) and (5-42), ,' /.., /.: /; are selected according to the sufficient conditions:

Sb22 2m2 (kb, + kb2) + wk-r 2
a > kb3 > -- 12 y2 > 2r, (5-49)
4 1 2Tm2

where M2, b, E R+, y R+ are 1. ,,i 1. in (5-2), (5-38), and (5-40), "i.' /:, and 7,

wo E R+ are -;,I1-. ;. ,'/1.;i 1. f;,. constants.

Remark 1. The second sufficient gain condition indicates that w can be selected suffi-

i, ,;/ small and kb, can be selected suff.- i nil, 1. lag, provided 1 2rM2 > 0. The condition









that 1 2Trm2 > 0 indicates that the constant approximation matrix B must be chosen

suff. :, ./il, close to M-'(q) so that iB M-'(q) I| < 2 Experimental results illustrate
the performance/robustness of the developed controller with respect to the mismatch be-
tween B and M-l(q). S1 .. ..:I.'ll; results indicate an <.:,"'.:,,'.:i. ., amount of variation in
the performance even when each element of M-l(q) is overestimated by as much as 10r "
Different results i,,rn be obtained for different s;,il 1ii, but these results indicate that the
gain condition is reasonable.
Proof: Let y(t) E )D C T.' 2 be defined as
T
t( [e e (5-50)

where P (t), Q (t) E R denote LK functionals defined as [45]

P W ( t u(0)2 d ds, Q 2b T/= 1 e2 2d
t-7J s t ,-

where w e R+ is a known constant. A positive definite Lyapunov functional candidate

V (y, t) : x [0 oo) R is defined as

1 1
V(y, t) 2A eTe1 + Te/M(q)e2 + P + Q, (551)

and satisfies the following inequalities

A l112 < V < A 2 1 (5-52)

where Ai, A2 E R+ are defined in (5-23).
Taking the time derivative of (5-51) and using (5-37) and (5-44) yields

V = -aeTe + eBez + w-r u u2 + S+ N 2 ., j [(q)i ( 2 2)]

+ [|2ekb 2 2 2- 2 2 (O) 2 dO, (5-53)
Jt-r

where the Leibniz integral rule was applied to determine the time derivative of P(t) (see
the Appendix 7.2) and Q(t). Using (5-2), (5-38), and (5-46), the terms in (5-53) can be








upper bounded as


V < -a ||e111 kb Ie2 + '1/i2 I. I 112 + + T IIU112 + le211 \ + IC 2 ||11 ) I|| I I + b I|el| ||e,1 |

I' 21 + 1kb [k, I 211 2 -2 2] _- u(0)112 d0. (5-54)

The following terms in (5-54) can be upper bounded by utilizing Young's inequality:


4 7
lTm2/ b 621 62 62 I+ I 62 112
2 2

where 7 E R+ is a known constant. Further, by using the Cauchy Schwarz inequality, the
following term in (5-55) can be upper bounded as

| 2 j I ()2 d0. (5-56)
t-T

After adding and subtracting f_ IU (0)) 12 dO to (5-54), and utilizing (5-42), (5-43),
(5-55) and (5-56), the following expression is obtained:

b2 2 1 27
V < -(a ) 6e112 (kb3 wkI2r 2ram2kb) 62e12 -1 ( 2) 1e,112
47 72

-kb, 2 + 2 (1) l lkb, el2 + 22 ~2E I()( 2 d0.(5-57)

By completing the squares, the inequality in (5-57) can be upper bounded as

P2 -
V < 2- f2 I I(12 d + 2C, (5-58)

where 32 E R+ is denoted as
b b2 7 2) 2 1 2,T
32 min a (k- GL 2,

Since

Iu(o0) 12 d s < T sup [ t u(0)112 d] 0 r u(O) 12do,
t- 1 sE[t,t-T] s t-T








the expression in (5-58) can rewritten as

< {- 2 41 2; IiU(O) 2d0 I I U(O) 2d0 + 42
< 0- 2 2 Y k b
(5 59)
Using the definitions of z(t) in (5-47), y(t) in (5-50), and u(t) in (5-42), the expression in
(5-59) can be expressed as

< I02- {/C2 2 I}e2 + (5-60)
S 4kb, 11 4kb (560)

where /2( :11I) e R is defined as


P4kb2 2rm2 2wy2
/2 -min(02 kb,17 2)JJ] "

By further utilizing (5-52), the inequality in (5-60) can be written as

S< 32 +- (5-61)
A2 4kb2

Consider a set S defined as

SA z{(t) E1 | < 1 (2
In S, /32(: 1I|) can be lower bounded by a constant 2 c R+ as

62 < / 2( II). (5-63)

Based on (5-63), the linear differential equation in (5-61) can be solved as

V < V(0)e-- + -22 (5-64)
4kb, 62

provided 1||| I< p21 (2 /32kb) From (5-64), if z(0) E S then kb can be chosen according
to the sufficient conditions in (5-49) (i.e. a semi-global result) to yield result in (5-48).
Based on the definition of y(t), it can be concluded that el(t), e2(t) E L in S. Given
that el(t), e2(t), qd(t), d(t) in S, (5-4), (5-42), and (5-37) indicate that q(t),

q(t), u e in S.









5.4 Experimental Results and Discussion

Experiments for the developed controllers were conducted on a two-link robot

shown in Fig. 5-1. Each robot link is mounted on an NSK direct drive switched


















Figure 5-1. Experimental testbed consiting of a 2-link robot. The input delay in the
system was artificially inserted in the control software.


reluctance motor (240.0 Nm Model YS5240-GN001, and 20.0 Nm Model YS2020-GN001,

respectively). The NSK motors are controlled through power electronics operating

in torque control mode. Rotor positions are measured through motor resolver with

a resolution of 614400 pulses/revolution. The control algorithms were executed on a

Pentium 2.8 GHz PC operating under QNX. Data acquisition and control implementation

were performed at a frequency of 1.0 kHz using the ServoToGo I/O board. Input delay

was artificially inserted in the system through the control software (i.e., the control

commands to the motors were d. 1 I, .1 by a value set by the user). The developed

controllers were tested for various values of input delay ranging from 1 ms to 200 ms.

The desired link trajectories for link 1 (qd (t)) and link 2 (qz(t)) were selected as (in

degrees):

qd (t) = qd,(t) = 20.0sin(1.5t)(l exp(-0.01t3)).









The controller developed in (5-8) (PID controller with 1/. ,.r; compensation) and the

controller developed in (5-42) (PD controller with /. ,.r; compensation) were compared

with traditional PID and PD controllers, respectively, in the presence of input delay in the

system. The input d, 1 .i1 two link robot dynamics are modeled as

Uir pi + 2p3 cos(q2) 2 + p3 cos(q2) i -3 sin(q2) 2 -p3 sin(q2)(g + q2)

2 P2 + p3 cos(q2) 2 ][2 P3 sin(q2)1 0

il fd, 0 i fsi 0 tanh(ql)

92 0 fd2 2 0 s2 tanh(q2)

where pi, P2, P3, fdi, fd2, fs, fs2 E R+ are unknown constants, and r E R+ is the

user-defined time delay value. However, the following values: pi = 3.473kg.m2, P2

0.196kg.m2, and ps = 0.242kg.m2 were used to calculate the inverse inertia matrix for

implementing the PID controller with 1/ /.,r; compensation but were not used to implement

the PD controller with 1/ /.,r; compensation.

The control gains for the experiments were obtained by choosing gains and then

adjusting based on performance (in particular, torque saturation). If the response

exhibited a prolonged transient response (compared with the response obtained with

other gains), the proportional gains were adjusted. If the response exhibited overshoot,

derivative gains were adjusted. At a particular input d. 1 iv value, the control gains were

first tuned for the PID/PD controllers with /1 /.,r; compensation and then compared with

traditional PID/PD controllers. Using the same control gains values as in the PID/PD

controllers with /. /.,;/ compensation, the control torques for the traditional PID/PD

controllers reached pre-set torque limits, leading to an incomplete experimental trial (e.g.,

if the control torque reaches 20 Nm, which is the set torque limit for the link-2 motor, the









control software aborts the experimental trial2 ). Therefore, for each case of input delay

(except at 1 ms), control gains for the traditional PID/PD controllers were retuned (i.e.,

lowered) to avoid torque saturation. In contrast to the above approach, the control gains

could potentially have been adjusted using more methodical approaches. For example, the

nonlinear system in [124] was linearized at several operating points and a linear controller

was designed for each point, and the gains were chosen by interpolating, or scheduling the

linear controllers. In [125], a neural network is used to tune the gains of a PID controller.

In [126] a genetic algorithm was used to fine tune the gains after initial guess were made

by the controller designer. The authors in [127] provide an extensive discussion on the use

of extremum seeking for tuning the gains of a PID controller. Additionally, in [128], the

tuning of a PID controller for robot manipulators is discussed.

The experimental results are summarized in Table 5-1. The error and torque plots

for the case when the input delay is 50 ms (as a representative example) are shown in

Figs. 5-3-5-4. The PD controller with /. /.,;/ compensation was also tested to observe

the sensitivity of the B gain matrix, defined in (5-37), where the input d.1 iv was

selected as 100 ms. Each element of the B gain matrix was incremented/decremented

by a certain percentage from the inverse inertia matrix (see Table 5-2). The purpose of

this set of experiments was to show that the gain condition discussed in Remark 1 is

a sufficient but not a necessary condition, and to explore the performance/robustness

of the controller in (5-42) given inexact approximations of the inertia matrix. The

controller exhibited no significant degradation, even when each element of the inertia

matrix is over-approximated by 1C(' However, underestimating the inverse inertia

matrix (particularly when deviation from the inverse inertia matrix was 75 percent),



2 Instead of aborting the experimental trial, the experiments could have also
been performed by utilizing the saturation torque as the control torque in case the
computed torque reaches or exceeds the torque limit; but for comparison purposes, the
aforementioned criterion was chosen.









yielded increased tracking errors. Different results may be obtained for different systems.

The third set of experiments, given in Table 5-3 were conducted to show that promising

results can be obtained even when the input delay value is not exactly known; however,

the tracking error performance degrades with increasing inaccuracy in delay value

approximation (e.g., in the case of PD + compensator, the tracking error increases

significantly when the d. 1 lv value is overestimated by 1 1'. or greater). For this set of

experiments the input delay was chosen to be 100 ms.

The experimental results clearly show that the PID/PD controllers with /. ,i ;

compensation perform better than the traditional PID/PD controllers. Both controllers

can be divided into respective PID/PD components and predictor (delay compensating)

terms. The better performance shown by the controllers can be attributed to the predictor

components in both the controllers. As an illustrative example, Fig. 5-2 shows the time

plots of the PD controller with /. /.I,; compensation and its control components. The

two components: PD component and delay compensating term are plotted to show

their behavior with respect to each other. The plot shows that the d. 1 li compensating

component is ah--iv-i following the PD component but is opposite in sign (like an mirror

image but less in magnitude). Thus, the net (actual) control torque is aliv-l less than

the PD control component. This implies that the delay compensating term tends to

correct the PD component (acts as a primary torque generator) which may have compiled

extraneous torque due to the input delay. The delay compensating term predicts the

correction term by finitely integrating control torque over the time interval ranging from

current time minus the time delay to current time.

5.5 Delay compensation in NMES through Predictor-based Control

The primary goal of the input delay research was to compensate for Electromechanical

delay (EMD) in NMES. EMD in muscle force generation is defined as the difference in

time from the arrival of action potential at the neuromuscular junction to the development

of tension in the muscle [8]. In NMES control, the EMD is modeled as an input delay



















10. s .
S- I







S20
-10 ...... .




0 1 2 3 4 5
Time [see.]

Figure 5-2. The plot shows three torque terms: PD component shown in dotted line, delay
compensating term plotted in dashed line, and the net or actual control torque shown in
solid line. The PD component and the delay compensating term (finite integral term of
control values) are two components of the PD controller with /. 1,;/ compensation (actual
control torque). Note that the delay compensating term is alvb--, opposite in sign to the
PD component. Thus, the net control torque is alvb--, less than the PD controller. This
implies that the delay compensating term tends to correct the PD component which may
have compiled extraneous torque due to the input delay. The predictor term computes the
correction term by finitely integrating control torque over the time interval ranging from
current time minus the time delay to current time.


in the musculoskeletal dynamics [6] and occurs due to finite conduction velocities of the

chemical ions in the muscle in response to the external electrical input [36]. Input d.1 iv

can cause performance degradation as was observed during NMES experimental trials

on human subjects with RISE and NN+RISE controllers and has also been reported to

potentially cause instability during human stance experiments with NMES [40].

5.5.1 Experiments: Input Delay Characterization

Experiments were conducted to characterize input delay in healthy individuals during

NMES. The tested consisted of LEM (detailed in Section 3.4.4.1). The delay in NMES









RMS Error
Controller PID PID + CPTR PD PD + CPTR
Time Delay Linki Link2 Linki Link2 Linki Link2 Linki Link2
1 ms 0.1060 0.0890 0.1090 0.0870 0.0770 0.0830 0.0770 0.0760
2 ms 0.1070 0.1250 0.1130 0.0920 0.0650 0.1510 0.0690 0.0650
5 ms 0.1290 0.3700 0.1150 0.0770 0.061 0.291 0.0760 0.0820
10 ms 0.0890 0.2850 0.1310 0.091 0.0570 0.5050 0.0890 0.0880
50 ms 1.9540 1.2720 0.3700 0.3350 1.0370 1.6020 0.4070 0.3360
100 ms 3.1370 6.6050 1.0780 0.7260 3.1820 5.5950 1.1590 0.7290
200 ms 7.6290 6.7780 3.1180 3.6260 14.5320 17.5860 3.6250 2.3750
Maximum Absolute Peak Error
1 ms 0.1640 0.1730 0.1690 0.1780 0.1240 0.1580 0.1270 0.1500
2 ms 0.1720 0.2300 0.1790 0.180 0.1050 0.2750 0.1140 0.1250
5 ms 0.2040 0.6420 0.1790 0.1610 0.1080 0.5090 0.1270 0.1500
10 ms 0.1490 0.5120 0.2070 0.2110 0.1070 0.7070 0.1470 0.2000
50 ms 3.4300 2.0680 0.671 1.1960 1.7760 2.9980 0.7740 1.1930
100 ms 6.4840 11.6030 1.9640 2.4150 5.9300 11.551 1.9150 2.3330
200 ms 14.9600 12.5690 6.6000 10.4660 24.6290 32.7260 5.5200 6.8780
Table 5-1. Summarized experimental results of traditional PID/PD controllers and the
PID/PD controllers with d. 1 iv compensation. The controllers were tested for different
input delay values ranging from 1 ms to 200 ms. CPTR stands for compensator.

Elementwise percentage change RMS Error
in inverse inertia matrix Linki Link2
0 1.1720 1.0050
+10 1.2460 1.1680
-10 1.0780 0.9550
-50 1.5830 1.491
+50 1.5400 1.2490
+100 1.1910 1.0860
-75 2.9480 1.331
Table 5-2. Results compare performance of the PD controller with delay compensation,
when the B gain matrix is varied from the known inverse inertia matrix. The input delay
value was chosen to be 100 ms. The results indicate that large variations in the gain
matrix may be possible.


was measured as the difference between the time when voltage is applied to the muscle

and the time when the angle encoder detects the first leg movement. The input delay

values were measured for ten healthy individuals (9 male and 1 female). The tests on

each individual investigated the effect on input delay of three stimulation parameters:

frequency, pulsewidth, and voltage. Three different set of tests including: frequency vs









RMS Error
Percent uncertainty PD + Compensator PID + Compensator
in input delay Linki Link2 Linki Link2
(O'. 1.1590 0.7300 1.0780 0.7260
S(+)10' 1.2340 0.9660 0.9370 0.9100
(-)1(' 1.0790 1.2150 0.7560 0.4100
(+)21i'. 1.3380 1.5480 1.3040 1.8100
(-)2i'- 1.1920 1.7730 0.7820 0.6170
(+):l I 1.451 1.761 1.4980 0.6590
S(-)S -i'. 1.4520 1.3220 0.7680 0.6090
(+)50' 1.6290 2.5130 2.2420 1.1810
(- )5(' 1.1860 1.4500 0.9870 0.9070
S(+ i'. 3.5280 6.8190 3.0920 1.5100
(- i'. 1.2290 5.4080 0.9150 2.0530
(+)91' 4.0990 12.0200 3.3220 1.8360
(-) 91i' 3.2600 6.041 0.8740 2.461
(+)10'. 4.331 12.4450 4.2190 3.1010
(-)1O1 '. 3.1820 5.5950 3.1370 6.6050
Table 5-3. Experimental results when the input d 1li has uncertainty. The input delay
value was selected as 100 ms.


input delay, voltage vs input delay, and pulsewidth vs input delay were performed on

each individual. In each set of experiments, the other two stimulation parameters were

kept constant. Before the start of experiments, the subject was instructed to relax to

avoid voluntary leg motion. The threshold voltage was measured for each subject which

can be defined as the minimum voltage applied to the subject's muscle that produces

a movement large enough to be detected by the angle encoder. This measurement was

performed by applying a constant input voltage, beginning at 10 V and increasing the

voltage slightly until movement was detected. Once the threshold voltage was obtained,

the aforementioned three sets of experiments were performed for each individual.

The first set of experiments constituted varying frequency while keeping voltage and

pulsewidth constant. These tests consisted of measuring the input delay of the subject's

muscle for three 0.2 second impulses, each 5 seconds apart. Each impulse imparted a

constant voltage (threshold voltage + 10 V) to the muscle. The 5 second time separation

between the impulses allowed the subjects to voluntarily bring their leg back to the





















Sr -

-3 -2
0 10 20 30 40 50 0 10 20 30 40 50
2 ^ I- i -f T -t

















0i 0



-3

0 10 20 30 40 50 0 10 20 30 40 50
Time [sec.] Time [sec.]


Figure 5-3. The top-left and bottom-left plots show the errors of Link 1 and Link 2,
respectively, obtained from the PID controller with delay compensation and a traditional
PID controller. The top-right and bottom-right plots show the errors of Link 1 and Link
2, respectively, obtained from the PD controller with delay compensation and a traditional
PD controller. Errors obtained from the PID/PD + delay compensator are shown as
solid lines and the errors obtained from the traditional PID/PD controller are shown as
dash-dot lines. The input delay was chosen to be 50 ms.


rest position. Fig. 5-5 shows the typical EMD during NMES in a healthy individual.

Final input delay value was computed by averaging the measured d. 1 values over three

impulses. Eight experiments were performed for different frequencies, where the frequency

was chosen randomly from the range of 30 Hz and 100 Hz (intra range interval of 10













E 21
I

00

, -1
-ii
; -21


0 10 20 30 40 50 0 10 20 30 40 50


I. I .i l .


10 20 30
Time [sec]


0 10 20 30
Time [sec]


Figure 5-4. The top-left and bottom-left plots show the torques of Link 1 and Link 2,
respectively, obtained from the PID controller with delay compensation. The top-right and
bottom-right plots show the torques of Link 1 and Link 2, respectively, obtained from the
PD controller with delay compensation. The input delay was chosen to be 50 ms.


Hz). The pulse width for this type of the experiments was kept at 100ps. The second

type of experiments consisted of varying pulsewidth while keeping voltage and frequency

constant. Each experiment constituted three impulses as explained above for the frequency

tests. Nine experiments were performed for different pulsewidths, where pulsewidth was

randomly chosen from 100ps. to 1000ps (intra range interval of 100Ms). For this set


40 50


. .l i .






















0 .5 .. : / .. ... ...
II. *






0.5 --- i
0 0.05 0.1 0.15 0.2 0.25 0.3
Time [sec.]

Figure 5-5. Typical input delay during NMES in a healthy individual. The desired
trajectory is shown in dashed line and the actual leg angle is shown in solid line. Note
that the actual leg angle starts rising around 70 ms.


of experiments, the frequency was kept constant at 30 Hz and the voltage consisted of

minimum threshold voltage + 10 V. The last set of experiments involved conducting

experiments with varying voltages. Same impulse program as used in the earlier set of

experiments was used, where pulsewidth and frequency were kept constant. The frequency

was kept at 30 Hz and the pulse width was kept at 100 mus. Three experiments were

performed for different voltages (threshold voltage + additional voltage, where additional

voltage was varied between 5 and 20 volts (intra range interval of 5 volts). Table 5-4 (as

a representative example) shows the summarized input delay variations with respect to

different stimulation parameters in a healthy individual.

ANOVA (Analysis of variance) tests were performed to determine the intraclass

correlations. An ANOVA test is generally employ, -1 to determine the statistical significance

between the means of data groups numbering more than two (using student t-test to











determine the statistical significance between more than two data groups can lead to

Type-I error (i.e., rejection of null hypothesis which in reality is true)). The results of

the stimulation frequency testing (see Fig. 5-6) showed that the difference in the means

of EMD was statistically significant (P-value = 1.50372E 10). Further, post-hoc test

utilizing Tukey's method showed that the EMD was longer for the lower frequencies than

for the higher frequencies. Particularly, the test showed that the average EMD of 76 ms

at a frequency of 30 Hz is statistically different from the average EMD of 51 ms at a

frequency of 100 Hz. However, the results of the stimulation pulse width (see Fig. 5-8) and

voltage experiments (see Fig. 5-7) showed no significant correlation between either varying

stimulation pulsewidth or stimulation voltage and electromechanical delay (P-value =

0.6870 and 0.072, respectively).


Frequency Vs. Time Delay
0.1


0.09

008 ISubject 1
-U-Subject2

-t-Subject4

2 \ -.m- Subject S
006 ..-Subject 6
Subject 7

005
SSubject9
--o- Subject 10
0.04
30 40 50 60 70 80 90 100

Frequency(Hz)

Figure 5-6. Average input delay values across different frequencies.



5.5.2 Experiments: PD Controller with Delay Compensation

The challenge in implementing the controllers in (5-8) and (5-42) is to measure

inertia and input delay in the muscle dynamics. Implementing the controller in (5-8)

becomes even more complicated due to the fact that it requires not only inertia of the












Time Delay vs Voltage
0.ii





0.09
S----Subject 1
0.08 "--"-- -----Subject 2
Subject
008 ---Subject 3

O -.," -*--Subject 4
S~-Subject S
NI- --^' ^- ~- -- --tSubject b


Subject 9
-@-Subject 10
0.05


0.04
5 7 9 11 13 15 17 19
Voltage


Figure 5-7. Average input delay values across different voltages.



musculoskeletal-LEM system to be measured but also the auxiliary function 6Q(q, c, t) E

R defined in (2-8), which consists of unmeasurable muscle force-velocity and muscle

force-length relationships to be known. However, the controller defined in (5-42) can be

implemented provided the following assumptions are made.

Assumption 1: The input delay is measurable and is constant. Although the input

delay for the NMES system is measurable but may not be constant due to v ,i i. I of

factors such as fatigue, non-isometric contractions, type of task, or stimulation parameters.

However, these variations are likely to be minimal in the duration of a single trial, and

the fact that the new controllers are shown to be robust to uncertainty in the input delay

value (see Table 5-3).

Assumption 2: The function JQ introduced in (3.3) can be upper bounded as



ai < JQ < 2, B J < a3 (5-65)


where al, a2, a3 E 2 are some known positive constants, and B is the control gain

introduced in (5-37).











Pulse Width vs. Time Delay

0.1


0.09

S Subject 1
0.08
S- Subject

0.07 /-- ---Subject4
SSubjecto5
0.06 -m--Subjtect6
SSubject7
0.05 Subject 9
SSubject 10
0.04
100 200 300 400 500 600 700 800 900 1000
Pulse Width (ps)


Figure 5-8. Average input delay values across different pulsewidths.


The tested for experiments consisted of LEM (detailed in Section 3.4.4.1). The

control objective was to track a continuous constant period (2 sec.) sinusoidal trajectory.

Three ble shmales (age: 21-28yrs) were chosen as the test subjects. After the protocol

(see section 3.4.4.1), the input delay value was measured for each subject. The measured

delay value was utilized for implementing the PD controllcr with I/ /,;/ compensation and

throughout the duration of trials, the same respective measured delay value was used

for each subject. The experiments compared the traditional PD controller with the PD

controller with Il/.,;I compensation. Each subject participated in two to four trials for each

controller 3 The experimental results obtained for each controller are summarized in Table

5-5. The table shows best two results (results with minimum RMS errors out of all trials)

obtained from each controller and subject.




3 maximum number of trials are limited due to increasing discomfort that arises due to
rapid muscle fatigue.









Frequency [Hz] Pulsewidth [p sec.] Voltage [V] Ti 72 73 Avg. 7
30 100 10 0.069 0.053 0.073 0.065
40 100 10 0.076 0.064 0.077 0.072
50 100 10 0.073 0.069 0.075 0.072
60 100 10 0.062 0.074 0.06 0.065
70 100 10 0.064 0.066 0.051 0.060
80 100 10 0.062 0.059 0.077 0.066
90 100 10 0.062 0.057 0.048 0.056
100 100 10 0.055 0.061 0.059 0.058
30 200 10 0.065 0.066 0.094 0.075
30 300 10 0.07 0.072 0.079 0.074
30 400 10 0.065 0.065 0.09 0.073
30 500 10 0.058 0.056 0.071 0.062
30 600 10 0.05 0.073 0.064 0.062
30 700 10 0.065 0.077 0.058 0.067
30 800 10 0.065 0.067 0.061 0.064
30 900 10 0.071 0.053 0.055 0.060
30 1000 10 0.057 0.083 0.065 0.068
30 100 5 0.081 0.061 0.061 0.068
30 100 15 0.068 0.079 0.087 0.078
30 100 20 0.082 0.084 0.059 0.075
Table 5-4. Summarized input d.l 1 values of a healthy individual across different
stimulation parameters. Delay values (7) are shown in seconds. The voltages shown in
column 3 are the added voltages to the threshold voltage.


A Student's t-test was also performed to confirm statistical significance in the mean

differences of the RMS errors, maximum steady state errors (SSEs), RMS voltages, and

the maximum voltages. The statistical comparison was conducted on the averages of the

two best results obtained for each subject. The analysis shows that the mean differences

in the RMS errors, maximum SSEs, and maximum voltages are statistically significant

while the analysis shows no statistical difference in the RMS voltages. The mean RMS

error of 4.43 obtained with the PD controller with 1/ Ir.' compensation is lower than the

RMS error of 6.030 obtained with the PD controller. Also, the mean maximum SSE and

the mean maximum voltage obtained with the PD controller with /. /I,;/ compensation are

lower than the mean maximum SSE and the mean maximum voltage obtained with the

traditional PD controller. The respective p-values are given in the Table 5-5. The actual









leg angle, error, and voltage plots obtained from subject C (as a representative example)

are shown in Figs. 5-9 and 5-10.


-7
4 0 o ... .. .... .


S0 5 10 15 20
20



0
0 5 10 15 20
Time [sec.]
4



0 5 10 15 20
Time [sec.]
40

30 30

20
0 5 10 15 20
Time [sec.]



Figure 5-9. Top plot: Actual limb trajectory of a subject (solid line) versus the desired
trajectory (dashed line) input obtained with the PD controller with /. r,'; compensation.
Middle plot: The tracking error (desired angle minus actual angle) of a subject's leg,
tracking a constant (2 sec.) period desired trajectory. Bottom plot: The computed voltage
of the PD controller with 1/ ,r;i compensation during knee joint tracking.


5.6 Conclusion

Control methods are developed for a class of an unknown Euler-Lagrange systems

with input delay. The designed controllers have a predictor-based structure to compensate

for dl 1 i,- in the input. LK functionals are constructed to aid the stability analysis which

yields a semi global uniformly ultimately bounded result. The experimental results

show that the developed controllers have improved performance when compared to
















0
0 5 10 15 20
Time [sec.]




P -10---
-20
0 5 10 15 20
Time [sec.]
50





0 5 10 15 20
Time [sec.]



Figure 5-10. Top plot: Actual limb trajectory of a subject (solid line) versus the desired
trajectory (dashed line) input, obtained with the traditional PD controller. Middle
plot: The tracking error (desired angle minus actual angle) of a subject's leg, tracking
a constant (2 sec.) period desired trajectory. Bottom plot: The computed PD voltage
during knee joint tracking. Note that the voltage saturates at the user-defined set lower
voltage threshold of 10 V


traditional PID/PD controllers in the presence of input delay. Additional experiments on

6. 11l!:1 individuals showed that the PD controller with delay compensation is capable to

compensate for input delay in NMES and also performs better than the traditional PD

controller. A key contribution is the development of the first ever controllers to address

delay in the input of an uncertain nonlinear system. The result has been heretofore an

open challenge because of the need to develop a stabilizing predictor for the dynamic

response of an uncertain nonlinear system. To develop the controllers, the time delay









RMS Error RMS Voltage [V] Max. SSE Max. Voltage [V]
Subject PD PD +CTR PD PD +CTR PD PD +CTR PD PD +CTR
A 4.480 5.260 31.49 33.18 11.840 11.51 42.95 42.02
A 7.630 3.520 29.30 32.26 20.41 9.040 50 44.38
B 8.480 6.350 20.93 22.93 25.780 9.6110 45.1 27.43
B 6.540 5.960 24.72 22.65 10.790 10.720 31.28 26.51
C 3.110 2.850 25.58 26.17 12.840 5.680 43.68 38.8
C 5.91 2.61 23.65 27.60 16.660 5.60 49.33 36.7
Mean 6.030 4.430 25.95 27.47 16.370 8.690 43.72 35.97
p value 0.003* 0.095 0.008* 0.040*
Table 5-5. Table compares the experimental results obtained from the traditional
PD controller and the PD controller with /. .',;/ compensation. indicates statistical
significance and CTR stands for compensator.


was required to be a known constant. While some applications have known d-.1 iv (e.g.,

teleoperation [129], some network d [-v.1l [130], time constants in biological systems

[6, 36]), the development of more generalized results (which have been developed for

some linear systems) with unknown time d.-1 i- remains an open challenge. However, the

experimental results with two-link robot illustrated some robustness with regard to the

uncertainty in the time delay.









CHAPTER 6
RISE-BASED ADAPTIVE CONTROL OF AN UNCERTAIN NONLINEAR SYSTEM
WITH UNKNOWN STATE DELAYS

6.1 Introduction

The development in this chapter is motivated by the lack of continuous robust

controllers that can achieve .,-i-i1l l ic stability for a class of uncertain time-d, 1 li'- 1

nonlinear systems with additive bounded disturbances. The approach described in the

current effort uses a continuous implicit learning [96] based Robust Integral of the Sign of

the Error (RISE) structure [11, 27]. Due to the added benefit of reduced control effort and

improved control performance, an adaptive controller in conjunction with RISE feedback

structure is designed. However, since the time d 1li value is not .liv -,i- known, it becomes

challenging to design a delay free adaptive control law. Through the use of a desired

compensation adaptive law (DCAL) based technique and segregating the appropriate

terms in the open loop error system, the dependence of parameter estimate laws on the

time d. 1 -, ,1 unknown regression matrix is removed. Contrary to previous results, there is

no singularity in the developed controller. A Lyapunov-based stability analysis is provided

that uses an LK functional along with Young's inequality to remove time d. 1 i. terms

and achieves .i-vmptotic tracking.

6.2 Problem Formulation

Consider a class of uncertain nonlinear systems with an unknown state delay as [87]


x = x2









x(t) = f(x(t)) + 61(x(t)) + g(x(t r)) + 62(x(t r)) + d(t) + bu(t)

y = x1 (6-1)









In (6-1), f(x(t)), 61(x(t)) e R2 are unknown functions, g(x(t r)), 2( (t r)) C RI
are unknown time-dl 1 i-, '1 functions, r E R+ is an unknown constant arbitrarily large

time delay, d(t) E R" is a bounded disturbance, b E R is an unknown positive constant,

u(t) C R" is the control input, and x(t) [xT X x... xT] T R"' denote system states,
where x(t) is assumed to be measurable. Also the following assumptions and notations will

be exploited in the subsequent development.

Notation: Throughout the paper, a time dependent d, 1 li- .1 function is denoted as
x(t r) or x,, and a time dependent function (without time delay) is denoted as x(t) or x.

Assumption 1: The unknown functions b- f(x), b-lg(x) are linearly parameterizable,
i.e., b-'f(x) Yi(x)01, b-'g(x) Y2x)02, where Yi(x) e R2 p, Y2x) C R"' are

regression matrices of known functions, 01 e RP21x, 02 e -.' -xl are constant unknown

parameter vectors, and pi, P2 are positive integers. The regression matrix Y2(xT) is not
computable due to the unknown time delay present in the state

Assumption 2: If x(t) E L, then g(x), 61(x), 62(x) are bounded. Moreover, the
first and second partial derivatives of g(x), 61(x), 62(x) with respect to x(t) exist and are

bounded (see [83, 87, 95]).

Assumption 3: The disturbance term and its first two time derivatives are bounded
(i.e., d(t), d(t), d(t) c L).
Assumption 4: The desired trajectory is designed such that yd(t), yd(t) cE where

yd)(t) denotes the ith time derivative for i = 1, 2,..., n + 2.
6.3 Error System Development

The control objective is to ensure that the output y(t) E R" tracks a desired

time-varying trajectory yd(t) E Rm despite uncertainties in the system and an unknown
time delay in the state. To quantify the objective, a tracking error, denoted by el(t) E R",
is defined as

el(t) Y (t) d(t). (6-2)









To facilitate the subsequent analysis, following filtered tracking errors are also defined as


e2(t) e1(t) + aleI(t), (6-3)


ei(t) A ei-(t) + ai- 1eCi(t) + ei-2, (6-4)

r(t) ,(t) + )aen(t), (6-5)

where ac, ..., aOn E R denote positive constant control gains. As defined in (6-5), the

filtered tracking error r(t) is not measurable since the expression depends on x,(t).

However, ei(t),...,en(t) E RI are measurable because (6-4) can be expressed in terms of

the tracking error el(t) as

i-1
ea(t) aije i 2,...,n, (6-6)
j=0

where aij E R are positive constants obtained from substituting (6-6) in (6-4) and

comparing coefficients [114]. It can be easily shown that


aij = 1, j =i 1. (6-7)


Using (6-2)-(6-7), the open loop error system can be written as


r= y() -y +) (6-8)


where l(el, ei,..., e 1)) e R'" is a function of known and measurable terms, defined as

n-2
S anj (+l) + anej) + (n-1)
j=0

The open-loop tracking error system can be developed by premultiplying (6-8) by b-1 and

utilizing the expressions in (6 1) and Assumption 1 to obtain the following expression:


b- r = Yl(x)01 + b-'1(x) + Y2(x)02 + b- 2(x,) + b-'d + u b- y" + b- l. (6-9)









In the subsequent development, a DCAL-based update law is developed in terms of Y2(')
without a state delay. After some algebraic manipulation, the expression in (6-9) can be
rewritten as

b-'r = b-' + S + S2 + W + b-d + Y,(d) + Y2(d)0 + u,

where the auxiliary functions Sl(xd, x), S2(Xdr, x ), W(xd, Xdr, ydj)) CE IR are defined as

S1 = YI()1 Y( (Xd)1 + b- l() b- 1(Xd), (6-10)


S2 Y2(x)2 Y2(Xd)2 + b-62(x,) b- 62(Xdr), (6-11)


W = b-61(Xd) + b- 2(Xd)- Y2(Xd)2 Y2(dr)2 b- ), (6-12)

where Xd [y yP y d ) T Rm" denotes a column vector containing the
desired trajectory and its derivatives. The grouping of terms and structure of (6-10) is
motivated by the subsequent stability analysis and the need to develop an adaptive update
law that is invariant to the unknown time delay. The auxiliary function Sl(xd, x) is defined
because these terms are not functions of the time-delay. The auxiliary function S2 (Xdr, Xr)
is introduced because the time-d'l 1' .1 states are isolated in this term, and W(xd, Xd,, ad)
is isolated because it only contains functions of the desired trajectory.
Based on the open-loop error system in (6-10), the control input u(t) E R" is
designed as
u = -YI(Xd)O1 Y2(xd)2 p. (6-13)

In (6-13), pE R"7 denotes the implicit learning-based [96] RISE term defined as the
generalized solution to

f = (kh + 1) r + 3sgn(en), p(O) = 0, (6-14)









where ks, 3E R are known positive constant gains. In (6-13), 01(t) E R1, 02(t)E -'
denote parameter estimate vectors defined as

01 = FlY (xd)r, 02 r2Y2 d)r, (6-15a)

where F1 e IRplxp1, F2 E -! ._P2 are known, constant, diagonal, positive definite adaptation
gain matrices. In (6-15a), Y2(xd) does not depend on the time d. 1 .1 desired state. This
delay free law is achieved by isolating the d, 1 li .1 term Y2(Xdr)02 in the auxiliary signal

W(xd, Xdr, y()) in (6 12). The adaptation laws in (6-15a) depend on the unmeasurable
signal r(t), but by using the fact that Y1(xd), Y2(Xd) are functions of the known time
varying desired trajectory, integration by parts can be used to implement 8 (t) for i = 1, 2
where only eT(t) is required as

0, 0(0) + PYIT(xd)eC.(7) 1 {Yxden a) x en } da.

The closed-loop error system can be developed by substituting (6-13) into (6-10) as

b-b-r = b + S +S2 + W +b-ld- + Yl(Xd) + Y2(Xd)O2, (6-16)

where Qi for i = 1, 2 are the parameter estimation error vectors defined as

0i = Oi 0i. (6-17)

To facilitate the subsequent stability analysis and to more clearly illustrate how the RISE
structure in (6-14) is used to reject the disturbance terms, the time derivative of (6-16) is
determined as

b-'r = N+Nd -en +Y((Xd)l +Y 2(Xd)2 -(ks +1) r sgn(e), (6-18)









where the auxiliary functions N(e, ...e, r, eli, ..., eT, r,), Nd(Xd, Xd, t) E Rm are defined as

N = b- i + S+ & S+ C, Y(xd) Fl(xd)r Y2(Xd2(Xd)r,

Nd W + b-'d. (6-19)

Using Assumptions 2, 3, and 4, Nd(xd, Xd, Xd, t) and its time derivative can be upper
bounded as

|Nd1 < (Nd, Nd < (NC (6-20)

where (Nd, (Nd E R are known positive constants. The expression defined in (6-19) can be
upper bounded using the Mean Value Theorem as [114]

N < pi(|| ||) 1|| 11 + p2(11 -- ) 1|| -| (6-21)

where z(t) E 1'1 m is defined as

z e e e r (6-22)

and the known bounding functions pi (|: ||), p2 (|| : I) E R are positive, globally invertible,
and nondecreasing functions. Note that the upper bound for the auxiliary function
N(el,e2, eC, e2.) in (6-21) is segregated into delay free and d. 1 i. '1 upper bound
functions. Motivation for this segregation of terms is to eliminate the delay dependent
term through the use of an LK functional in the stability analysis. Specifically, let

Q(t) E R denote an LK functional defined as

Q =- = p (||-(o ||) ||(::( )||2 ) da, (6-23)

where k E cR and p2a) are introduced in (6-14) and (6-21), respectively.
6.4 Stability Analysis
Theorem 6. The controller given in (6-13), (6-14), and (6-15a) ensures that all system
i.:l,.l are bounded under closed-loop operation. The tracking error is regulated in the









sense that


||ei(t)|| 0 as t oo,

provided the control gain ks introduced in (6-14) is selected suff .:. nill; 1I, and anl,

a0, and 3 are selected according to the following sufficient conditions:


3> (N, + N, a n-1, an > 2 (6-24)
( 1 ) 1 )

where a,_1, an are introduced in (6-4) and (6-5), ,. i. 1,:; /;3 is introduced in (6-14);
and (Nd and CNd are introduced in (6-20).
Proof: Let )D C )- p1) +p+p2+2 be a domain containing y(t) = 0, where y(t) e
R(n+ )mpl P2 +2 is defined as


y(t) A T P(t)T Q(t) ] (6-25)

where 6 (t) are defined in (6-17), z(t) and Q(t) are defined in (6-22) and (6-23),

respectively, and the auxiliary function P (t) E R is the generalized solution to the
differential equation
n
P (t) -L (t), P (0) =3 |e, (0) -e (0)TNd (0) (6 26)
i= 1
The auxiliary function L (t) E R in (6-26) is defined as

L (t) ^ rT (Nd (t) psgn (e,)). (6-27)

Provided the sufficient conditions stated in Theorem 6 are satisfied, then P (t) > 0 (see the
Appendix B).

Let VL (y, t) : D x [0, oc) IR denote a Lipschitz continuous regular positive definite
functional defined as


V( A 10 1A 22 1 1
V(y,t) ee+ ee2 ... + e + rb- + P + + O 0r1i (6 28)

+2 r2 12,









which satisfies the following inequalities


U (y) < V (y, t)< U2 (y), (6-29)

provided the sufficient conditions introduced in Theorem 6 are satisfied. In (6-29),

U1 (y) U2 (y) E R are continuous, positive definite functions defined as

Ui (y) = 71 Iy112 U2 (y) =7 I12 (6-30)

where 71, 72 E R are defined as

1
71 2- min(1, b- 7min {11}, F min {21}),

72 = max( -b-' l, rx {r1 (max { 1}),6 (6-31)

and 7min { }, 7max { } denote the minimum and maximum Eigenvalues, respectively. After
taking the time derivative of (6-28), VL (y, t) can be expressed as


VL (y, t) A e 2i + ee2 + + tb- +P+Q+ Q 1 + 0 0 + e1E02.

From (6-3), (6-4), (6-18), (6-26), (6-27), adaptation laws in (6-15a), and the time

derivative of Q(t) in (6-23), some of the differential equations describing the closed-loop
system for which the stability analysis is being performed have discontinuous right-hand









sides as


ei = e2 alel, (6-32a)

e2 = 63 a262 el, (6-32b)

(6-32c)

(6-32d)

(6-32e)

,n = r cien (6-32f)

b-'r 1 + Nd e, + Y(xd)1 + Y2(xd)02 (k, + 1) r sgn(en), (6-32g)

P (t) -rT (Nd (t) psgn (en)), (6-32h)
Q((t) 2 1(p((t))|() |() 2 ( T)) (t )2), (632i)
M (t ,\ I2(112 (6-32i)
2k, 2 2

0FI = -o Y (Xd)r, (6-32j)

oF2 1 2 2Y (Xd)r. (6-32k)

Let f(y, t) e I. 1)m+pi+p2+2 denote the right hand side of (6-32). f(y, t) is continuous

except in the set {(y,t)le2 = 0}. From [103-106], an absolute continuous Filippov solution

y(t) exists almost everywhere (a.e.) so that

e K[f](y,t) a.e.

The generalized time derivative of (6-28) exists a.e., and VL(y, t) a.e. VL (y, t) where







1
1 1 1 0 0
c C CT-1 2P 1 2Ql T F n 7 1- 1-1 F
1 2 .T T b- 2} 2 11 2F2IK 5 2 .' .

'-P Q O 2T.








For more details of the notations used in 6-32 to 6-33 and discussion, see Section 3.3.1.
After utilizing (6-3), (6-4), (6-18), (6-26), (6-27), adaptation laws in (6-15a) and the
time derivative of Q(t) in (6-23), the expression in (6-33) can be rewritten as


VL (y,t) C


-calele1 -

+r Y2 (d)2

- [T (Xd)r


- areee + e e -1 + eCr + r N + TNd rTe, + rYl(xd)l

(k, + 1) ||r|2 TK[sgn(en)]- rTNd(t) + OT K[sgn(e)]

022T(X)r + i) ) 2) (6-34)
02Y2Xd)F2hiP2H


Cancelling common terms yields and using (6-21)
n
(L {y> t) c ^ Q' o |e 1 112 + eT_ler -- r112 k, Ir12 + 112(11 ) :: -II I1r|1 + pl(1 11) 11 1,111 r 11
i=1
2-11 (11) 1 11 2(11 H11) 112
+ 22ks 2 2k (6-35)
2k, 2k,

After applying following Young's inequality to determine that

P2( -) 2 < l2 e llen 2 ei (6-36)
the ex2kn 2 n) cn be w n

the expression in (6-35) can be written as


n-2
-- |, 2 (an-1 1) en-1 2
i 1
2 1- I II k 11
2k8


(a,


Se 2- llr12
2} en F


After completing the squares, the expression in (6-37) can be written as

p2v I 1 I 1


(6-37)


where p2(1 11) IR is defined as


(6-38)


and 73 min [1i, a 2, o, n2, o.n- -. o 1 1] The bounding function p (| )
is a positive, globally invertible, and nondecreasing function that does not depend on


VL (y, t) C


2(11 ::(t)ll) P2(11 ::(t)l) + p2(11 :(t)lII),








the time-delay. The expression in (6-37) can be further upper bounded by a continuous,
positive semi-definite function

VL (Y,t) C -U(y) = -c :I- Vy CD (6-39)

for some positive constant c, where

-A y (t) e I I) m+pi+p +2 < -i1 2

Larger values of k, will expand the size of the domain D. The inequalities in (6-29)
and (6-39) can be used to show that V(y, t) E L, in D; hence, C1, 2,...,en, 01, 02 cE L
in D. The closed-loop error systems can now be used to conclude all remaining signals
are bounded in D, and the definitions for U(y) and z(t) can be used to prove that U(y) is
uniformly continuous in D. Let S C D denote a set defined as

S y(t) CD U2((t)) < 1 (1 )) (6-40)

The region of attraction in (6-40) can be made arbitrarily large to include any initial
conditions by increasing the control gain k, (i.e., a semi-global stability result), and hence

cll : '- 0 as t oo Vy(0) e S. (6-41)

Based on the definition of z(t), (6-41) can be used to show that

||ei(t)|| -0 as t oo Vy(0) e S. (6-42)

6.5 Simulations
To illustrate the performance of the RISE-based adaptive controller, we consider the
following first order scalar nonlinear plant [87]:

'i -= 2 (6-43)

2 = f(x) + g(x) + 61(x) + 62(x) + d + bu,









where f(x), g(x,) are linearly parameterizable functions, 61(x) is an unknown function,

62 (x) is an unknown d. 1 ',. 1 function, d(t) is a disturbance term, u(t) is the control
input, and b is an unknown coefficient. Since the time delay is unknown, the regression

matrix for g(x,) is unknown to the controller. However, the adaptive estimate laws

in the controller do not require the time delay value to be known. For the simulation

purposes, these functions and parameters are chosen: f(x) = 0.5sin(xl(t)); g(x,) =

0.2x(t 7r) + 2 cos(x2(t)); 61(X) = sin(5x2(t)); 62(xS) = 0.52(t- T) sin(2xi(t 7));

d = O.lsi(t); b = 1. The simulations are performed for the two cases of unknown time

d-.1 i-, namely, r = 3 s; r = 10 s. The desired trajectory is chosen as


Xd(t) = 0.5 [sin(t) + sin(0.5t)]. (6-44)

The following gains are chosen for r = 3 s and r = 10 s


ks = 10, aci 7, a2 6, /3 5, Fi = 0.5,

Fa = [2,0; 0,10].

From the results shown in Figs. 6-1-6-5, it is clear that the controller tracks the time

varying desired trajectory effectively. In both the cases, the steady state errors stay

between 0.003 radians and the control inputs are bounded. Also it can be seen that there

is a little variation in the control performances for time d,1 l' ,- = 3 s and r = 10 s.

6.6 Conclusion

A robust continuous RISE-based structure is utilized in conjunction with an adaptive

controller for stabilizing a class of uncertain nonlinear systems with unknown state d'-1 .,-

and bounded disturbances. By properly utilizing a DCAL-based method and segregating

the necessary terms, the controller and the adaptive estimate law do not depend on the

unknown time delay in the state. Appropriate LK functional is constructed to cancel

the time d,1 liv .1 terms in the stability analysis. Simulations are provided to show the















0.01





-0.01







-0.03
0 .03 '--------- --------
0 5 10 15 20
Time [sec]

Figure 6-1. Tracking error for the case r = 3 s.
















-2

-4

0 5 10 15 20
Time [sec]

Figure 6-2. Control input for the case r = 3 s.


performance of the controller. A Lyapunov-based stability analysis proves .-i, ,!l I ic

stability for the closed loop nonlinear system.




















1.5 .5. .- .. .. .. ... .


5 10 15
Time [sec]


Figure 6-3. Parameter estimates for the case = 3 s. Dashed line shows the parameter
estimate of 01. Solid line shows the parameter estimate of 02(1). Dash-dot line shows the
parameter estimate of 02(2).


5 10 15
Time [sec]


Figure 6-4. Tracking error for the case r


10 s.
































0 5 10 15
Time [sec]

Figure 6-5. Control input for the case r


20


10 s.


.i..... -... .: ...- ; :;............:)..- .- -,- .....- .-. -. -.. .


1.5


I .... ......... .............. .


0.5..




Time [sec]

Figure 6-6. Parameter estimates for the case r = 10 s. Dashed line shows the parameter
estimate of 01. Solid line shows the parameter estimate of 02(1). Dash-dot line shows the
parameter estimate of 02(2).









CHAPTER 7
CONCLUSION AND FUTURE WORK

7.1 Conclusion

New nonlinear controllers are developed to tackle various technical challenges in

implementing NMES. These difficulties include unknown nonlinear mapping between

the applied voltage to the muscle and the force generated in the muscle, bounded

disturbances, muscle fatigue, and time delay. The first two controllers developed in

C'! lpter 3 deal with unknown nonlinear 1 IlpplH-ii bounded disturbances, and other

unknown nonlinearities and uncertainties. The Lyapunov-based stability analysis

is utilized to prove semi-global .I-vmptotic stability for the controllers. Extensive

experiments on healthy volunteers were conducted for both RISE and NN+RISE

controllers. Particularly, it was shown that the inclusion of neural network based

feedforward component in the RISE controller improves performance during NMES. Also,

preliminary experimental trials demonstrating sit-to-stand task depicted the feasibility of

the NN+RISE controller in a clinical-type scenario.

In C'! lpter 4, a NN-based controller is developed to compensate for fatigue. The

benefit of the controller is that it incorporates more muscle dynamics knowledge namely,

calcium and fatigue dynamics. The effectiveness of the controller to compensate fatigue is

shown through simulation results. Further simulations show that the controller performs

better than the RISE controller.

An important technical difficulty in NMES is input delay which becomes more

challenging due to the presence of unknown nonlinearities and disturbances. Lack of

input delay compensating controllers for uncertain nonlinear systems motivated to

develop predictor-based controllers for general Euler Lagrange system in C'! lpter 5.

The Lyapunov-based stability analysis utilizes LK functionals to prove semi-global UUB

tracking. Extensive experimental results show better performance of the controller in

comparison to the traditional PD/PID controller as well as their robustness to uncertainty









in input delay value and inertia matrix. Further, the feasibility of the predictor-based

controller for NMES is shown through experimental trials on healthy individuals. Also, a

study to characterize input delay in NMES is included in the chapter which shows that the

input delay is dependent on frequency.

The last chapter in the dissertation covers the development of RISE-based adaptive

controller for a class of nonlinear system with state d. 1 ,i- The significance of the result is

that a robust and continuous controller is developed for a nonlinear system with unknown

state d, 1 li- and additive disturbances. Lyapunov-based stability analysis aided with LK

functionals is utilized to show a semi-global .,-i-, !! II l ic tracking.

7.2 Future Work

The following points discuss future work that can be built on the current research

described in the dissertation.

* Current experiments focused extensively on testing controllers on healthy
volunteers. These experiments showed that the controllers hold potential for clinical
tasks. Also, a preliminary test with the NN+RISE controller showed a promising
sit-to-stand task performance. Therefore, extensive experiments can be performed
where controllers should be tested on patients for functional tasks such as walking
and sit-to-stand maneuvers.

Efforts in Chapter 3 showed that the RMS error difference (for both RISE and
NN+RISE controllers) between the flexion and extension phase of the leg movement
is statistically significant. These results -ii.;. -1 that the role of switching controllers
(hybrid control approach) can be investigated. Specifically, two different controllers
can be utilized where each controller is dedicated for a particular phase of the leg
movement.

The result developed in C(i lpter 4 has three main limitations: unmeasurable
calcium and fatigue dynamics, dependence on acceleration, and uniformly ultimately
bounded stability result. Efforts can be made to develop an observer-based controller
to remove the dependence on mathematical fatigue and calcium dynamics models.
Specifically, recurrent neural network based observer can be designed to identify
system states. Further, improvement in stability analysis can be achieved by
developing a controller with .-i- i!!ill ic tracking. An extensive investigation is
required to observe the effect of the controller in C(i lpter 4 on reducing fatigue.
Experiments should not only compare the result with an existing controller
for improved performance but should specifically study the effectiveness of the
included fatigue model for fatigue compensation. The results may (or may not)









point to a need for improved fatigue models that are more suitable for non-isometric
contractions and account for multiple factors affecting the fatigue onset in NMES.
Also, additional information can be gathered to predict fatigue onset through
incorporating Electromyogram (EMG) signals. Measuring surface EMG signals can
be used as an indicator or can be utilized to quantify the fatigue onset which can be
further incorporated in NMES control design.

S Currently most of the NMES control implementation utilize single modulation
methods (e.g., the experiments were performed with amplitude modulation
technique, where the frequency and pulsewidth were kept constant while voltage
is varied). Methods can be developed to modulate multiple stimulation parameters
simultaneously. However, more efforts will be required first to investigate the effects
of multiple modulation during NMES control. The benefits of this research may
manifest as improved control performance during fatigue onset (e.g., frequency i' '-
an important role in the fatigue onset. Modulating frequency along with amplitude
may delay the onset of fatigue during NMES.)

* One of the most important technical issue in NMES is the rapid onset of fatigue.
Numerous factors influence the early onset of fatigue during NMES control.
Overstimulation due to high gain controller is one of the factors that affects the
fatigue onset. Feedforward methods or using low gain control are alv-i- I -,'::-. -1. I
to avoid early onset of fatigue. However, high gain controllers are required to obtain
minimal tracking errors during functional tasks. A solution to optimize these two
conflicting strategies can be obtained by designing optimal controllers. Proper
mechanisms can be built into these controllers to provide a choice between better
error performance or delaying the fatigue onset.

* The focus of the current research was mainly on developing control techniques
for non-invasive surface electrical stimulation. The main disadvantage of surface
electrical stimulation is repetitive and non-selective recruitment of muscle fibres
which lie in the path of applied current. This type of muscle recruitment is the
main cause of rapid fatigue onset and is in contrast to the recruitment employ,
by the brain and central nervous system during voluntary contractions. In context
to this disadvantage, researchers have used invasive electrodes to stimulate specific
muscles or nerves in the paralyzed patients to produce desired functional movements.
The main benefit of these methods is selective and non-repetitive recruitment of
muscle fibres, thereby avoiding muscle fatigue. However, wires protruding out from
the skin and chances of infection have made this option unattractive. With the
advancement of technology, some researchers have developed micro-stimulators
called BIONs [131], which can be surgically implanted at specific sites in the muscle.
These microelectrodes which do not require wires are powered externally through
an inductive coil and a battery. Multiple BIONs to stimulate specific muscle sites
can not only be used to produce desired functional movements but also can be used
to eliminate muscle fatigue through utilizing non-repetitive and selective muscle
recruitment. In order to produce NMES control via BIONs, studies will be required









to imitate the strategies used by brain and central nervous system during voluntary
contractions. The real challenge will be to maintain stability and coordination of
multiple implanted BIONs in order to extract desired movements. Approaches from
hybrid control theory and co-operative control should be investigated to develop
NMES control via BIONs.

Development focused on input delay measurement in the C'! lpter 5 showed that
the input delay in NMES depends only on varying frequency. However, further
investigations are required to study the effect of fatigue and non-isometric
contractions on input delay. Also, results in C'! lpter 5 are only applicable with
known constant input delay values. Therefore, controllers need to be developed
to account for time-varying or unknown input delay. Other delay compensating
techniques such as model predictive control (\!PC) can also be investigated for
NMES. One of the advantages of MPC is that it inherently compensates for input
d- 1 i- Although the technique would require muscle dynamics to be known,
advantages such as performance and control optimization in addition to d. 1li
compensation makes MPC a worthy candidate for investigation.








APPENDIX A
(CHAPTER 5) PREDICTOR-BASED CONTROL FOR AN UNCERTAIN
EULER-LAGRANGE SYSTEM WITH INPUT DELAY
Lemma 1. D. fiu. Q(t) E R as

Q(t)= Y (1 (o (0) 11 ds.

The time derivative of Q(t) is
The time derivative of Q(t) is


Q(t=) = rL u(t)112


U (o)2 1dO..
t-T


Proof: The time derivative of Q(t)


dQ) [-T


on applying Leibniz integral rule can be written as


Q(t) t ( 11t(od 2dO dt


(/i'-T


d-T
dt ]-


(1-3)


The expression in (1-3) can be simplified as


t ()t ( ||/ a t
it(o) 11\2 do + L -
J-- -- s


Again applying Leibniz integral rule on second integral in (1-4)


t -t
(0o) \2 do + Ij j() 1 2 d
The expression in (15) can be simplified a

The expression in (1-5) can be simplified as


Further integrating the second integral in (1-6)


UrT Iu(t) 11


(1-1)


(1-2)


\s /


ds] I


jt(0)112 d0 ds.


(1 4)


ii s) 2 2) d ds.
11 d1 t+ at1()1 o


(1-5)


j I |(0)2 dO + a i(t)2 It ds.
Jt- 1-7


(1-6)


St(o) 112 de.


It-T


I(o)112 d6o) d(









Lemma 2. D. fiu P(t) E R as


P(t) = ( uIIu(e0) 2d) ds.


The time derivative of P(t) is
The time derivative of P(t) is


P(t) = r u(t) 2I


Proof: The proof is similar to the proof given for Lemma 1


(1-7)


rt
t_-T


(1-8)









APPENDIX B
(CHAPTER 6) RISE-BASED ADAPTIVE CONTROL OF AN UNCERTAIN
NONLINEAR SYSTEM WITH UNKNOWN STATE DELAYS

Lemma 3. D. fin, L(t) E R as


Then, if 0 -,/.:/,


then


L A rT(Nd psgn(e,)).




S> wNd
> Nd + -a


L (7) dT <
i= 1


(0) 1 eC (O)T Nd (0) ,


where ei (0) E R denotes the ith element of the vector en (0).

Proof: Integrating both sides of (2-9)


JL(a)da
0o


f [rT(Nd
Jo


Osgn(en))] da.


(2-12)


On substituting (6-5) in (2-12) yields


L L(o)du


It
n Nddu
Jon d
) ~.l


)t )t
0 en0sgne)d + j0 aeNd
Jo Jo


Osgn(en))da.


(2 13)


After utilizing integration by parts for the first integral and integrating the second integral

in (2-13), the following expression is obtained:


L(jo)d eTNd
0 n


Jot
e(N~s


n
e (0) Nd (0) + e(0)|
i 1


where the fact that sgn(en) can be denoted as


sgn(en) = [sgn(enl) sgn(en2)


(2-9)


(2-10)


(2-11)


d1 1)d l\
d- _psgn(en)) da,
a dao


i= 1


(2-14)


gn(e,,,)] ,


(2-15)









is utilized in the second integral. Using the bounds given in (6-20) and the fact that


-(t) || <
i 1

the expression in (2-14) can be upper bounded as


t
I L(,)du <
0n


(0 1 )
i 1


3 e~ )+jci eJ


((N, +


S-d da.
a


(2-17)


It is clear from (2-17) that if the following sufficient condition


/3> N +
a


is satisfied, then the following inequality holds


t n
L(a)d 0 i= 1


(t) ,


(2-16)


(2 18)


e,(0) N(0).


(2-19)









REFERENCES


[1] P. H. Peckham and D. B. Gray, "Functional neuromuscular stimulation," J. Rehabil.
Res. Dev., vol. 33, pp. 9-11, 1996.

[2] P. H. Peckham and J. S. Knutson, "Functional electrical stimulation for
neuromuscular applications," Annu. Rev. Biomed. Eng., vol. 7, pp. 327-360, 2005.

[3] J. J. Abbas and H. J. Chizeck, 1. I II ., 1 control of coronal plane hip angle in
paraplegic subjects using functional neuromuscular stimulation," IEEE Trans.
Biomed. Eng., vol. 38, no. 7, pp. 687-698, 1991.

[4] N. Lan, P. E. Crago, and H. J. Chizeck, "Control of end-point forces of a multijoint
limb by functional neuromuscular stimulation," IEEE Trans. Biomed. Eng., vol. 38,
no. 10, pp. 953-965, 1991.

[5] 1', I 1I I: control methods for task regulation by electrical stimulation of
muscles," IEEE Trans. Biomed. Eng., vol. 38, no. 12, pp. 1213-1223, 1991.

[6] T. Schauer, N. O. Negard, F. Previdi, K. J. Hunt, M. H. Fraser, E. Ferchland, and
J. Raisch, "Online identification and nonlinear control of the electrically stimulated
quadriceps muscle," Control Eng. Pract., vol. 13, pp. 1207-1219, 2005.

[7] K. Stegath, N. Sharma, C. M. Gregory, and W. E. Dixon, "An extremum seeking
method for non-isometric neuromuscular electrical stimulation," in Proc. IEEE Int.
Conf. Syst. Man. C;,1,. ,,. 2007, pp. 2528-2532.

[8] A. H. Vette, K. Masani, and M. R. Popovic, IpllI.!, i il I :ii' of a physiologically
identified PD feedback controller for regulating the active ankle torque during quiet
stance," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 2, pp. 235-243, June
2007.

[9] G. Khang and F. E. Z i, 1 "Paraplegic standing controlled by functional
neuromuscular stimulation: Part I computer model and contr .l--i-I. i1 design,"
IEEE Trans. Biomed. Eng., vol. 36, no. 9, pp. 873-884, 1989.

[10] F. Previdi, M. Ferrarin, S. Savaresi, and S. Bittanti, "Gain scheduling control
of functional electrical stimulation for assisted standing up and sitting down in
paraplegia: a simulation study," Int. J. Adapt Control S.:i,,Il Process., vol. 19, pp.
327-338, 2005.

[11] P. M. Patre, W. MacKunis, C. Makkar, and W. E. Dixon, "Asymptotic tracking for
systems with structured and unstructured uncertainties," IEEE Trans. Control Syst.
Technol., vol. 16, no. 2, pp. 373-379, 2008.

[12] "Asymptotic tracking for systems with structured and unstructured
uncertainties," in Proc. IEEE Conf. Decis. Control, San Diego, CA, Dec. 2006,
pp. 441-446.









[13] N. Lan, H. F iw- and E. Crago, \. i, I network generation of muscle stimulation
patterns for control of arm movements," IEEE Trans. Rehabil. Eng., vol. 2, no. 4,
pp. 213-224, 1994.

[14] J. J. Abbas and H. J. Chizeck, \N .i I! network control of functional neuromuscular
stimulation systems: computer simulation studies," IEEE Trans. Biomed. Eng.,
vol. 42, no. 11, pp. 1117-1127, Nov. 1995.

[15] D. Graupe and H. Kordylewski, "Artificial neural network control of FES in
paraplegics for patient responsive ambulation," IEEE Trans. Biomed. Eng., vol. 42,
no. 7, pp. 699-707, July 1995.

[16] G.-C. C'!I(I,- J.-J. Lub, G.-D. Liao, J.-S. Lai, C.-K. C'I. 1, B.-L. Kuo, and T.-S.
Kuo, "A neuro-control system for the knee joint position control with quadriceps
stimulation," IEEE Trans. Rehabil. Eng., vol. 5, no. 1, pp. 2-11, Mar. 1997.

[17] J. A. Riess and J. J. Abbas, "Adaptive neural network control of cyclic movements
using functional neuromuscular stimulation," IEEE Trans. Neural Syst. Rehabil.
Eng., vol. 8, pp. 42-52, 2000.

[18] H. Kordylewski and D. Graupe, "Control of neuromuscular stimulation for
ambulation by complete paraplegics via artificial neural networks," Neurol. Res.,
vol. 23, no. 5, pp. 472-481, 2001.

[19] D. G. Zhang and K. Y. Zhu, "Simulation study of FES-assisted standing up with
neural network control," in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.,
vol. 6, 2004, pp. 4118-4121.

[20] J. P. Giuffrida and P. E. Crago, "Functional restoration of elbow extension after
spinal-cord injury using a neural network-based synergistic FES controller," IEEE
Trans. Neural Syst. Rehabil. Eng., vol. 13, no. 2, pp. 147-152, 2005.

[21] Y.-L. Chen, W.-L. C('!, i C.-C. Hsiao, T.-S. Kuo, and J.-S. Lai, "Development of the
FES system with neural network + PID controller for the stroke," in Proc. IEEE
Int. Symp. Circuits Syst., May 23-26, 2005, pp. 5119-5121.

[22] K. Kurosawa, R. Futami, T. Watanabe, and N. Hoshimiya, "Joint angle control by
FES using a feedback error learning controller," IEEE Trans. Neural Syst. Rehabil.
Eng., vol. 13, pp. 359-371, 2005.

[23] A. Pedrocchi, S. Ferrante, E. De Momi, and G. Ferrigno, "Error mapping controller:
a closed loop neuroprosthesis controlled by artificial neural networks," J. Neuroeng.
Rehabil., vol. 3, no. 1, p. 25, 2006.

[24] S. Kim, M. Fairchild, A. Iarkov, J. Abbas, and R. Jung, "Adaptive control for
neuromuscular stimulation-assisted movement therapy in a rodent model," IEEE
Trans. Biomed. Eng., vol. 56, pp. 452-461, 2008.









[25] A. Ajoudani and A. Erfanian, "A neuro-sliding-mode control with adaptive modeling
of uncertainty for control of movement in paralyzed limbs using functional electrical
stimulation," IEEE Trans. Biomed. Eng., vol. 56, no. 7, pp. 1771-1780, Jul. 2009.

[26] J. Lujan and P. Crago, "Automated optimal coordination of multiple-DOF
neuromuscular actions in feedforward neuroprostheses," IEEE Trans. Biomed.
Eng., vol. 56, no. 1, pp. 179-187, Jan. 2009.

[27] P. M. Patre, W. MacKunis, K. Kaiser, and W. E. Dixon, "Asymptotic tracking
for uncertain dynamic systems via a rmiiltili, r neural network feedforward and
RISE feedback control structure," IEEE Trans. Autom. Control, vol. 53, no. 9, pp.
2180-2185, 2008.

[28] M. J. Levy, M. and Z. Susak, "Recruitment, force and fatigue characteristics of
quadriceps muscles of paraplegics, isometrically activated by surface FES," J.
Biomed. Eng., vol. 12, pp. 150-156, 1990.

[29] D. Russ, K. Vandenborne, and S. Binder-Macleod, I-i'. in fatigue during
intermittent electrical stimulation of human skeletal muscle," J. Appl. Ph; -.,1
vol. 93, no. 2, pp. 469-478, 2002.

[30] J. Mizmhi, "Fatigue in muscles activated by functional electrical stimulation," Crit.
Rev. Phys. Rehabil. Med., vol. 9, no. 2, pp. 93-129, 1997.

[31] E. Asmussen, \Iiiucle fatigue," Med. Sci. Sports. Exerc., vol. 11, no. 4, pp. 313-321,
1979.

[32] R. Maladen, R. Perumal, A. Wexler, and S. Binder-Macleod, "Effects of activation
pattern on nonisometric human skeletal muscle performance," J. Appl. Phi;i-./ vol.
102, no. 5, pp. 1985-91, 2007.

[33] S. Binder-Macleod, J. Dean, and J. Ding, "Electrical stimulation factors in
potentiation of human quadriceps femoris," Muscle Nerve, vol. 25, no. 2, pp. 271-9,
2002.

[34] Y. Giat, J. Mizrahi, and M. Levy, "A musculotendon model of the fatigue profiles of
paralyzed quadriceps muscle under FES," IEEE Trans. Biomed. Eng., vol. 40, no. 7,
pp. 664-674, 1993.

[35] R. Riener, J. Quintern, and G. Schmidt, "Biomechanical model of the human knee
evaluated by neuromuscular stimulation," J. Biomech., vol. 29, pp. 1157-1167, 1996.

[36] R. Riener and T. Fuhr, "Patient-driven control of FES-supported standing up: A
simulation study," IEEE Trans. Rehabil. Eng., vol. 6, pp. 113-124, 1998.

[37] J. Ding, A. Wexler, and S. Binder-Macleod, "A predictive fatigue model. I.
predicting the effect of stimulation frequency and pattern on fatigue," IEEE Trans.
Rehabil. Eng., vol. 10, no. 1, pp. 48-58, 2002.









[38] "A predictive fatigue model. II. predicting the effect of resting times on
fatigue," IEEE Trans. Rehabil. Eng., vol. 10, no. 1, pp. 59-67, 2002.

[39] S. Jezernik, R. Wassink, and T. Keller, "Sliding mode closed-loop control of FES:
Controlling the shank movement," IEEE Trans. Biomed. Eng., vol. 51, pp. 263-272,
2004.

[40] K. Masani, A. Vette, N. Kawashima, and M. Popovic, N iuromusculoskeletal
torque-generation process has a large destabilizing effect on the control mechanism of
quiet standing," J. N -u iph; i vol. 100, no. 3, p. 1465, 2008.

[41] S. Evesque, A. Annaswamy, S. Niculescu, and A. Dowling, "Adaptive control of a
class of time delay systems," J. Dyn. Syst. Meas. Contr., vol. 125 (2), pp. 186-193,
2003.

[42] J. Huang and F. Lewis, \. il il -network predictive control for nonlinear dynamic
systems with time-delay," IEEE Trans. Neural Networks, vol. 14, no. 2, pp. 377-389,
2003.

[43] D. Yanakiev and I. Kanellakopoulos, "Longitudinal control of automated CHVs
with significant actuatc. '. 1 i- IEEE Trans. Veh. Technol., vol. 50, no. 5, pp.
1289-1297, 2001.

[44] B. Bequette, "Nonlinear control of chemical processes: A review," Ind. Eng. C'hl
Res., vol. 30, no. 7, pp. 1391-1413, 1991.

[45] J.-P. Richard, "Time-delay systems: an overview of some recent advances and open
problems," Automatica, vol. 39, no. 10, pp. 1667 1694, 2003.

[46] K. Gu, V. L. Kharitonov, and J. C'! ii Si,7l.ill, of Time-,.1 I.i; s.l-/.i Birkhauser,
2003.

[47] W. Kwon and A. Pearson, 1,, II 1 1: stabilization of linear systems with d. 1 .i1 1
control," IEEE Trans. Autom. Control, vol. 25, no. 2, pp. 266-269, 1980.

[48] Z. Artstein, "Linear systems with d, 1 i, '1 controls: A reduction," IEEE Trans.
Autom. Control, vol. 27, no. 4, pp. 869-879, 1982.

[49] Y. Fiagbedzi and A. Pearson, 1, -. I11I I. 1: stabilization of linear autonomous time lag
systems," IEEE Trans. Autom. Control, vol. 31, no. 9, pp. 847-855, 1986.

[50] M. Jankovic, "Recursive predictor design for linear systems with time delay," in
Proc. IEEE Am. Control Conf., June 2008, pp. 4904-4909.

[51] A. Manitius and A. Olbrot, "Finite spectrum assignment problem for systems with
d.-1.-,- IEEE Trans. Autom. Control, vol. 24, no. 4, pp. 541-552, 1979.









[52] S. Mondi6 and W. Michiels, "Finite spectrum assignment of unstable time-delay
systems with a safe implementation," IEEE Trans. Autom. Control, vol. 48, no. 12,
pp. 2207-2212, 2003.

[53] Y. Roh and J. Oh, "Robust stabilization of uncertain input-d. 1 li systems by sliding
mode control with delay compensation," Automatica, vol. 35, pp. 1861-1865, 1999.

[54] M. Krstic, "Lyapunov tools for predictor feedbacks for delay systems: Inverse
optimality and robustness to delay mismatch," Automatica, vol. 44, no. 11, pp.
2930-2935, 2008.

[55] M. Krstic and D. Bresch-Pietri, "D( li-- i1 iptive full-state predictor feedback for
systems with unknown long actuator delay," in Proc. IEEE Am. Control Conf., 2009,
pp. 4500-4505.

[56] D. Bresch-Pietri and M. Krstic, "Adaptive trajectory tracking despite unknown
input delay and plant parameters," Automatica, vol. 45, no. 9, pp. 2074-2081, 2009.

[57] M. Krstic and A. Smyshlyaev, "Backstepping boundary control for first-order
hyperbolic PDEs and application to systems with actuator and sensor d,-i1- Syst.
Contr. Lett., vol. 57, no. 9, pp. 750-758, 2008.

[58] W. Michiels, K. Engelborghs, P. Vansevenant, and D. Roose, "Continuous pole
placement for delay equations," Automatica, vol. 38, no. 5, pp. 747-761, 2002.

[59] O. M. Smith, "A controller to overcome deadtime," ISA J., vol. 6, pp. 28-33, 1959.

[60] M. Matausek and A. Micic, "A modified smith predictor for controlling a process
with an integrator and long dead-time," IEEE Trans. Autom. Control, vol. 41, no. 8,
pp. 1199-1203, Aug 1996.

[61] S. A 1 i1 and D. Atherton, "Modified smith predictor and controller for processes
with timedelay," IEE Proc. Contr. Theor. Appl., vol. 146, no. 5, pp. 359-366, 1999.

[62] W. Zhang and Y. Sun, "Modified smith predictor for controlling integrator/time
delay processes," Ind. Eng. C.I ,, Res., vol. 35, no. 8, pp. 2769-2772, 1996.

[63] A. Nortcliffe and J. Love, "Varying time delay smith predictor process controller,"
ISA Trans., vol. 43, no. 1, pp. 61-71, 2004.

[64] P. Garcia and P. Albertos, "A new dead-time compensator to control stable and
integrating processes with long dead-time," Automatica, vol. 44, no. 4, pp. 1062
1071, 2008.

[65] S. AM, 11i and D. Atherton, "Obtaining controller parameters for a new smith
predictor using autotuning," Automatica, vol. 36, no. 11, pp. 1651-1658, Nov 2000.

[66] I. Chien, S. Peng, and J. Liu, "Simple control method for integrating processes with
long deadtime," J. Process Control, vol. 12, no. 3, pp. 391-404, 2002.









[67] L. Roca, J. Luis Guzman, J. E. Normey-Rico, M. Berenguel, and L. Yebra, "Robust
constrained predictive feedback linearization controller in a solar desalination plant
collector field," Control Eng. Pract., vol. 17, no. 9, pp. 1076-1088, Sep. 2009.

[68] C. Xiang, L. Cao, Q. Wang, and T. Lee, "Design of predictor-based controllers for
input-delay systems," in Proc. IEEE Int. Symp. Ind. Electron., 30 2008-July 2 2008,
pp. 1009-1014.

[69] H.-H. Wang, "Optimal vibration control for offshore structures subjected to wave
loading with input d. 1 .i," in Int. Conf. Meas. Technol. Mechatron. Autom., vol. 2,
April 2009, pp. 853-856.

[70] "Optimal tracking for discrete-time systems with input d,-!-, ," in Proc. Chin.
Control Decis. Conf., July 2008, pp. 4033-4037.

[71] H.-H. Wang, N.-P. Hu, and B.-L. Z!i ili- "An optimal control regulator for nonlinear
discrete-time systems with input d-1iv -," in World Congr. Intell. Control Autom.,
June 2008, pp. 5540-5544.

[72] M. Krstic and A. Smyshlyaev, Bou,,I .,;i1 control of PDEs: A course on Backstepping
Designs. SIAM, 2008.

[73] S. Niculescu and A. Aim -i-- iii:r, "An adaptive smith-controller for time-delay
systems with relative degree n*<2," Syst. Contr. Lett., vol. 49, no. 5, pp. 347-358,
2003.

[74] C. Kravaris and R. Wright, "Deadtime compensation for nonlinear processes,"
AIChE J., vol. 35, no. 9, pp. 1535-1542, 1989.

[75] M. Henson and D. Seborg, "Time delay compensation for nonlinear processes," Ind.
Eng. Ch'. ,, Res., vol. 33, no. 6, pp. 1493-1500, 1994.

[76] F. Mazenc and P. Bliman, "Backstepping design for time-delay nonlinear systems,"
IEEE Trans. Autom. Control, vol. 51, no. 1, pp. 149-154, 2006.

[77] M. Jankovic, "Control of cascade systems with time delay the integral cross-term
approach," in Proc. IEEE Conf. Decis. Control, Dec. 2006, pp. 2547-2552.

[78] A. Teel, "Connections between Razumikhin-type theorems and the ISS
nonlinearsmall gain theorem," IEEE Trans. Autom. Control, vol. 43, no. 7, pp.
960-964, 1998.

[79] M. Krstic, "On compensating long actuator d. 1 li- in nonlinear control," IEEE
Trans. Autom. Control, vol. 53, no. 7, pp. 1684-1688, 2008.

[80] Y. Xia and Y. Jia, "Robust sliding-mode control for uncertain time-delay systems:
an LMI approach," IEEE Trans. Autom. Control, vol. 48, no. 6, pp. 1086-1091, June
2003.









[81] X.-J. Jing, D.-L. Tan, and Y.-C. Wang, "An LMI approach to stability of systems
with severe time-delay," IEEE Trans. Autom. Control, vol. 49, no. 7, pp. 1192-1195,
July 2004.

[82] S. Nguang, "Robust stabilization of a class of time-delay nonlinear systems," IEEE
Trans. Autom. Control, vol. 45, no. 4, pp. 756-762, 2000.

[83] S. Ge, F. Hong, and T. H. Lee, "Adaptive neural network control of nonlinear
systems with unknown time d. 1 ,- IEEE Trans. Autom. Control, vol. 48, no. 11,
pp. 2004-2010, Nov. 2003.

[84] S. Mondie and V. Kharitonov, "Exponential estimates for retarded time-delay
systems: an LMI approach," IEEE Trans. Autom. Control, vol. 50, no. 2, pp.
268-273, Feb. 2005.

[85] D. Ho, L. Junmin, and Y. Niu, "Adaptive neural control for a class of nonlinearly
parametric time-delay systems," IEEE Trans. Neural Networks, vol. 16, pp. 625-635,
2005.

[86] X. Li and C. de Souza, "Delay-dependent robust stability and stabilization of
uncertain linear delay systems: a linear matrix inequality approach," IEEE Trans.
Autom. Control, vol. 42, no. 8, pp. 1144-1148, Aug 1997.

[87] S. Ge, F. Hong, and T. Lee, "Robust adaptive control of nonlinear systems with
unknown time d-1iv -," Automatica, vol. 41, no. 7, pp. 1181-1190, Jul. 2005.

[88] S. Zhou, G. Feng, and S. Nguang, "Comments on "robust stabilization of a class of
time-delay nonlinear systems," IEEE Trans. Autom. Control, vol. 47, no. 9, 2002.

[89] S. J. Yoo, J. B. Park, and Y. H. Choi, "Comments on "adaptive neural control for a
class of nonlinearly parametric time-d, 1li systems"," IEEE Trans. Neural Networks,
vol. 19, no. 8, pp. 1496-1498, Aug. 2008.

[90] D. Ho, J. Li, and Y. Niu, "Reply to "comments on "adaptive neural control for
a class of nonlinearly parametric time-delay systems""," IEEE Trans. Neural
Networks, vol. 19, no. 8, pp. 1498-1498, Aug. 2008.

[91] K. Shyu and J. Yan, "Robust stability of uncertain time-delay systems and its
stabilization by variable structure control," Int. J. Control, vol. 57, no. 1, pp.
237-246, 1993.

[92] F. Gouaisbaut, W. Perruquetti, and J. P. Richard, "A sliding mode control for linear
systems with input and state d-.1 -," in Proc. IEEE Conf. Decis. Control, vol. 4,
Dec. 1999, pp. 4234-4239.

[93] F. Gouaisbaut, M. Dambrine, and J. P. Richard, "Sliding mode control of TDS
via functional surfaces," in Proc. IEEE Conf. Decis. Control, vol. 5, Dec. 2001, pp.
4630-4634.









[94] F. Gouaisbaut, M. Dambrine, and J. Richard, "Robust control of delay systems: a
sliding mode control design via LMI," Syst. Contr. Lett., vol. 46, no. 4, pp. 219-230,
2002.

[95] B. Mirkin, P. Gutman, and Y. Shtessel, "Continuous model reference adaptive
control with sliding mode for a class of nonlinear plants with unknown state delay,"
in Proc. IEEE Am. Control Conf., 2009, pp. 574-579.

[96] Z. Qu and J. X. Xu, "Model-based learning controls and their comparisons using
Lyapunov direct method," Asian J. Control, vol. 4(1), pp. 99-110, 2002.

[97] M. Ferrarin and A. Pedotti, "The relationship between electrical stimulus and joint
torque: A dynamic model," IEEE Trans. Rehabil. Eng., vol. 8, no. 3, pp. 342-352,
2000.

[98] J. L. Krevolin, M. G. Pandy, and J. C. Pearce, "Moment arm of the patellar tendon
in the human knee," J. Biomech., vol. 37, pp. 785-788, 2004.

[99] W. L. Buford, Jr., F. M. Ivey, Jr., J. D. Malone, R. M. Patterson, G. L. Peare, D. K.
Nguyen, and A. A. Stewart, i\!lucle balance at the knee moment arms for the
normal knee and the ACL minus knee," IEEE Trans. Rehabil. Eng., vol. 5, no. 4,
pp. 367-379, 1997.

[100] 0. M. Rutherford and D. A. Jones, \!. i-i cement of fibre pennation using
ultrasound in the human quadriceps in vivo," Eur. J. Appl. Phii; .,l vol. 65, pp.
433-437, 1992.

[101] R. Nathan and M. Tavi, "The influence of stimulation pulse frequency on the
generation of joint moments in the upper limb," IEEE Trans. Biomed. Eng., vol. 37,
pp. 317-322, 1990.

[102] T. Watanabe, R. Futami, N. Hoshimiya, and Y. Handa, "An approach to a muscle
model with a stimulus frE -,ii' ~ --,-force relationship for FES applications," IEEE
Trans. Rehabil. Eng., vol. 7, no. 1, pp. 12-17, 1999.

[103] A. Filippov, "Differential equations with discontinuous right-hand side," Am. Math.
Soc. Transl., vol. 42, pp. 199-231, 1964.

[104] Differential equations with discontinuous right-hand side. Netherlands: Kluwer
Academic Publishers, 1988.

[105] G. V. Smirnov, Introduction to the theory of differential inclusions. American
Mathematical Society, 2002.

[106] J. P. Aubin and H. Frankowska, Set-valued i,..l ;-!' Birkhuser, 2008.

[107] F. H. Clarke, Optimization and nonsmooth i..li-! SIAM, 1990.









[108] B. Paden and S. Sastry, "A calculus for computing Filippov's differential inclusion
with application to the variable structure control of robot manipulators," IEEE
Trans. Circuits Syst., vol. 34, pp. 73-82, 1987.

[109] D. Shevitz and B. Paden, "Lyapunov stability theory of nonsmooth systems," IEEE
Trans. Autom. Control, vol. 39, pp. 1910-1914, 1994.

[110] R. Riener and J. Quintern, Biomechanics and Neural Control of Posture and
movement, J. Winters and P. E. Crago, Eds. Springer-Verlag New York, Inc, 2000.

[111] J. Hausdorff and W. Durfee, "Open-loop position control of the knee joint using
electrical stimulation of the quadriceps and hamstrings," Med. Biol. Eng. Comput.,
vol. 29, pp. 269-280, 1991.

[112] F. L. Lewis, R. Selmic, and J. Campos, Neuro-F,..;. Control of Industrial S,.-/, -
with Actuator Nonlinearities. Philadelphia, PA, USA: Society for Industrial and
Applied Mathematics, 2002.

[113] F. L. Lewis, D. M. Dawson, and C. Abdallah, Robot Manipulator Control Theory
and Practice. CRC, 2003.

[114] B. Xian, M. de Queiroz, and D. Dawson, "A continuous control mechanism for
uncertain nonlinear systems," in Optimal Control, Stabilization and Nonsmooth
A,,l,;.'.: ser. Lecture Notes in Control and Information Sciences. Heidelberg,
Germany: Springer, 2004, vol. 301, pp. 251-264.

[115] C. Makkar, G. Hu, W. G. Sawyer, and W. E. Dixon, "Lyapunov-based tracking
control in the presence of uncertain nonlinear parameterizable friction," IEEE Trans.
Autom. Control, vol. 52, no. 10, pp. 1988-1994, 2007.

[116] N. Sharma, K. Stegath, C. M. Gregory, and W. E. Dixon, "Nonlinear neuromuscular
electrical stimulation tracking control of a human limb," IEEE Trans. Neural Syst.
Rehabil. Eng., vol. 17, no. 6, pp. 576-584, Dec. 2009.

[117] N. Sharma, C. M. Gregory, M. Johnson, and W. E. Dixon, "Modified neural
network-based electrical stimulation for human limb tracking," in Proc. IEEE Int.
Symp. Intell. Control, Sep. 2008, pp. 1320-1325.

[118] C. M. Gregory, W. Dixon, and C. S. Bickel, I1p I of varying pulse frequency and
duration on muscle torque production and fatigue," Muscle and Nerve, vol. 35, no. 4,
pp. 504-509, 2007.

[119] F. Z, ii '\! ,ucle and tendon: properties, models, scaling, and application to
biomechanics and motor control," Crit. Rev. Biomed. Eng., vol. 17, no. 4, pp.
359-411, 1989.

[120] H. Hatze, "A myocybernetic control model of skeletal muscle," B':. 1.'.. .'1 C,1., .,I /-
ics, vol. 25, no. 2, pp. 103-119, 1977.









[121] R. Happee, lii', i-.- dynamic optimization including muscular dynamics, a new
simulation method applied to goal directed movements," J. Biomech., vol. 27, no. 7,
pp. 953-960, 1994.

[122] F. L. Lewis, \X. i network control of robot manipulators," IEEE Expert, vol. 11,
no. 3, pp. 64-75, 1996.

[123] M. Ferrarin, F. Palazzo, R. Riener, and J. Quintern, "Model-based control of
FES-induced single joint movements," IEEE Trans. Neural Syst. Rehabil. Eng.,
vol. 9, no. 3, pp. 245-257, Sep. 2001.

[124] N. Stefanovic, M. Ding, and L. Pavel, "An application of L2 nonlinear control and
gain scheduling to erbium doped fiber amplifiers," Control Eng. Pract., vol. 15, pp.
1107-1117, 2007.

[125] T. Fuilii I1: Y. Kishida, M. Yoshioka, and S. Omatu, "Stabilization of double
inverted pendulum with self-tuning neuro-PID," in Proc. IEEE-INNS-ENNS Int.
Joint Conf. Neural Netw., vol. 4, 24-27 July 2000, pp. 345-348.

[126] F. N I, ii i K. Kuril --Vi-hi K. Kiguchi, and K. Watanabe, "Simulation of fine gain
tuning using genetic algorithms for model-based robotic servo controllers," in Proc.
Int. Sym. Comput. Intell. Robot. Autom., 20-23 June 2007, pp. 196-201.

[127] N. J. Killingsworth and M. Krstic, "PID tuning using extremum seeking: online,
model-free performance optimization," IEEE Contr. Syst. Mag., vol. 26, no. 1, pp.
70-79, 2006.

[128] R. Kelly, V. Santibanez, and A. Loria, Control of Robot Manipulators in Joint Space.
Springer, 2005.

[129] R. Anderson, M. Spong, and N. Sandia National Labs., Albuquerque, "Bilateral
control of teleoperators with time delay," IEEE Trans. Autom. Control, vol. 34,
no. 5, pp. 494-501, 1989.

[130] G. Liu, J. Mu, D. Rees, and S. C'! n, "Design and stability analysis of networked
control systems with random communication time delay using the modified MPC,"
Int. J. Control, vol. 79, no. 4, pp. 288-297, 2006.

[131] T. Cameron, G. Loeb, R. Peck, J. Schulman, P. Strojnik, and P. Troyk,
\ili romodular implants to provide electrical stimulation of paralyzed muscles
and limbs," IEEE Trans. on Biomed. Eng., vol. 44, no. 9, pp. 781-790, 1997.









BIOGRAPHICAL SKETCH

Nitin Sharma was born in November 1981 in Amritsar, India. He received his

Bachelor of Engineering degree in industrial engineering from Thapar University, India.

After his graduation in 2004, he was hired as a graduate engineer trainee from 2004 to

2005 and worked as an executive engineer from 2005 to 2006 in Maruti Suzuki India Ltd.

He then joined the Nonlinear Controls and Robotics (NCR) research group to pursue

his doctoral research under the advisement of Dr. Warren E. Dixon. He will be joining

as a postdoctoral fellow in Dr. Richard Stein's laboratory at the University of Alberta,

Edmonton, Canada.





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Iwouldliketoexpresssinceregratitudetomyadvisor,Dr.WarrenE.Dixon,forgivingmetheopportunitytoworkwithhim.IthankhimforexposingmetovastandexcitingresearchareaofnonlinearcontrolandmotivatingmetoworkonNeuromuscularElectricalStimulation(NMES)controlproblem.Ihavelearnttremendouslyfromhisexperienceandappreciatehissignicantroleindevelopingmyprofessionalskillsandcontributingtomyacademicsuccess.Iwouldalsoliketothankmyco-advisorDr.ChrisGregoryforansweringmyqueriesrelatedtomusclephysiologyandforguidingmeinbuildingcorrectprotocolsduringNMESexperiments.IalsoappreciatemycommitteemembersDr.ScottBanks,Dr.CarlD.CraneIIIandDr.JacobHammerforthetimeandhelptheyprovided.IwouldliketothankmycolleaguesfortheirsupportandappreciatetheirsteadfastvolunteeringinNMESexperiments.Iwouldliketothankmywifeforherloveandpatience.Also,Iwouldliketoattributemyoverallsuccesstomymotherwhotookhertimeandeorttoteachmeduringmychildhood.Finally,Iwouldliketothankmyfatherforhisbeliefinme. 4

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page ACKNOWLEDGMENTS ................................. 4 LISTOFTABLES ..................................... 8 LISTOFFIGURES .................................... 9 ABSTRACT ........................................ 11 CHAPTER 1INTRODUCTION .................................. 14 1.1MotivationandProblemStatement ...................... 14 1.2Contributions .................................. 22 2MUSCLEACTIVATIONANDLIMBMODEL .................. 25 3NONLINEARNEUROMUSCULARELECTRICALSTIMULATION(NMES)TRACKINGCONTROLOFAHUMANLIMB .................. 30 3.1Introduction ................................... 30 3.2ControlDevelopment .............................. 31 3.3NonlinearNMESControlofaHumanLimbviaRobustIntegralofSignumofError(RISE)method ............................ 32 3.3.1StabilityAnalysis ............................ 34 3.3.2ExperimentalResults .......................... 38 3.3.2.1Testbedandprotocol ..................... 39 3.3.2.2Resultsanddiscussion .................... 40 3.3.3Conclusion ................................ 46 3.4ModiedNeuralNetwork-basedElectricalStimulationforHumanLimbTracking ..................................... 48 3.4.1Open-LoopErrorSystem ........................ 50 3.4.2Closed-LoopErrorSystem ....................... 51 3.4.3StabilityAnalysis ............................ 55 3.4.4ExperimentalResults .......................... 59 3.4.4.1Testbedandprotocol ..................... 59 3.4.4.2Resultsanddiscussion .................... 61 3.4.5Limitations ............................... 68 3.4.6Conclusion ................................ 71 4NONLINEARCONTROLOFNMES:INCORPORATINGFATIGUEANDCALCIUMDYNAMICS ............................... 73 4.1Introduction ................................... 73 4.2MuscleActivationandLimbModel ...................... 73 5

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.............................. 76 4.3.1Open-LoopErrorSystem ........................ 77 4.3.2Closed-LoopErrorSystem ....................... 79 4.3.3BacksteppingErrorSystem ....................... 81 4.4StabilityAnalysis ................................ 81 4.5Simulations ................................... 84 4.6Conclusion .................................... 85 5PREDICTOR-BASEDCONTROLFORANUNCERTAINEULER-LAGRANGESYSTEMWITHINPUTDELAY .......................... 89 5.1Introduction ................................... 89 5.2DynamicModelandProperties ........................ 90 5.3ControlDevelopment .............................. 91 5.3.1Objective ................................. 91 5.3.2ControldevelopmentgivenaKnownInertiaMatrix ......... 91 5.3.3ControldevelopmentwithanUnknownInertiaMatrix ........ 97 5.4ExperimentalResultsandDiscussion ..................... 103 5.5DelaycompensationinNMESthroughPredictor-basedControl ...... 106 5.5.1Experiments:InputDelayCharacterization .............. 107 5.5.2Experiments:PDControllerwithDelayCompensation ....... 113 5.6Conclusion .................................... 117 6RISE-BASEDADAPTIVECONTROLOFANUNCERTAINNONLINEARSYSTEMWITHUNKNOWNSTATEDELAYS .................. 120 6.1Introduction ................................... 120 6.2ProblemFormulation .............................. 120 6.3ErrorSystemDevelopment ........................... 121 6.4StabilityAnalysis ................................ 125 6.5Simulations ................................... 130 6.6Conclusion .................................... 131 7CONCLUSIONANDFUTUREWORK ...................... 135 7.1Conclusion .................................... 135 7.2FutureWork ................................... 136 APPENDIX APREDICTOR-BASEDCONTROLFORANUNCERTAINEULER-LAGRANGESYSTEMWITHINPUTDELAY .......................... 139 BRISE-BASEDADAPTIVECONTROLOFANUNCERTAINNONLINEARSYSTEMWITHUNKNOWNSTATEDELAYS .................. 141 REFERENCES ....................................... 143 6

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................................ 153 7

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Table page 3-1TabulatedresultsindicatethatthetestsubjectwasnotlearningthedesiredtrajectorysincetheRMSerrorsarerelativelyequalforeachtrial. ........ 40 3-2Experimentalresultsfortwoperioddesiredtrajectory ............... 41 3-3Summarizedexperimentalresultsformultiple,higherfrequenciesandhigherrangeofmotion. ................................... 43 3-4SummarizedexperimentalresultsandPvaluesofonetailedpairedT-testfora1.5secondperioddesiredtrajectory. ........................ 63 3-5SummarizedexperimentalresultsandPvaluesofonetailedpairedT-testfordualperiodic(4-6second)desiredtrajectory. .................... 66 3-6Experimentalresultsforstepresponseandchangingloads ............ 66 3-7ThetableshowstheRMSerrorsduringextensionandexionphaseofthelegmovementacrossdierentsubjects, ......................... 71 5-1SummarizedexperimentalresultsoftraditionalPID/PDcontrollersandthePID/PDcontrollerswithdelaycompensation. ........................ 108 5-2ResultscompareperformanceofthePDcontrollerwithdelaycompensation,whentheBgainmatrixisvariedfromtheknowninverseinertiamatrix. .... 108 5-3Experimentalresultswhentheinputdelayhasuncertainty.Theinputdelayvaluewasselectedas100ms. ............................ 109 5-4Summarizedinputdelayvaluesofahealthyindividualacrossdierentstimulationparameters. ...................................... 116 5-5TablecomparestheexperimentalresultsobtainedfromthetraditionalPDcontrollerandthePDcontrollerwithdelaycompensation. .................. 119 8

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Figure page 2-1Muscleactivationandlimbmodel. ......................... 25 2-2Theleftimageillustratesaperson'sleftleginarelaxedstate. .......... 27 3-1Topplots:Actualleftlimbtrajectoryofasubject(solidline)versusthedesiredtwoperiodictrajectory(dashedline)input. .................... 42 3-2Topplot:Actuallimbtrajectory(solidline)versusthedesiredtripleperiodictrajectory(dashedline). ............................... 43 3-3Topplot:Actuallimbtrajectory(solidline)versusthedesiredconstantperiod(2sec)trajectory(dashedline). ........................... 44 3-4Topplot:Actuallimbtrajectory(solidline)versusthetripleperiodicdesiredtrajectorywithhigherrangeofmotion(dashedline). ............... 45 3-5Topplot:Actuallimbtrajectory(solidline)versusthedesiredconstantperiod(6sec)trajectory(dashedline). ........................... 46 3-6Topplot:Actuallimbtrajectory(solidline)versusdesiredsteptrajectory(dashedline). .......................................... 47 3-7Thetopplotshowstheactuallimbtrajectory(solidline)obtainedfromtheRISEcontrollerversusthedesired1.5secondperioddesiredtrajectory(dashedline). 61 3-8Thetopplotshowstheactuallimbtrajectory(solidline)obtainedfromtheNN+RISEcontrollerversusthedesired1.5secondperioddesiredtrajectory(dashedline). 62 3-9Thetopplotshowstheactuallimbtrajectory(solidline)obtainedfromtheRISEcontrollerversusthedualperiodicdesiredtrajectory(dashedline). ....... 64 3-10Thetopplotshowstheactuallimbtrajectory(solidline)obtainedfromtheNN+RISEcontrollerversusthedualperiodicdesiredtrajectory(dashedline). ....... 65 3-11ExperimentalplotsforstepchangeandloadadditionobtainedfromNN+RISEcontroller. ....................................... 67 3-12Initialsittingpositionduringsit-to-standexperiments.Theknee-anglewasmeasuredusingagoniometerattachedaroundtheknee-axisofthesubject'sleg. ...... 68 3-13Thetopplotshowstheactuallegangletrajectory(solidline)versusdesiredtrajectory(dottedline)obtainedduringthestandingexperiment. ........ 69 4-1Anuncertainfatiguemodelisincorporatedinthecontroldesigntoaddressmusclefatigue.Bestguessestimatesareusedforunknownmodelparameters. ..... 76 9

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.................................. 84 4-3Topplotshowsthekneeangleerrorfora2secondperiodtrajectoryusingtheproposedcontroller. .................................. 85 4-4Topplotshowsthekneeangleerrorfora6secondperiodtrajectoryusingtheRISEcontroller. .................................... 86 4-5Topplotshowsthekneeangleerrorfora2secondperiodtrajectoryusingtheRISEcontroller. .................................... 87 4-6RISEcontrollerwithfatigueinthedynamics .................... 87 4-7Performanceoftheproposedcontroller ....................... 88 4-8Fatiguevariable .................................... 88 5-1Experimentaltestbedconsitingofa2-linkrobot.Theinputdelayinthesystemwasarticiallyinsertedinthecontrolsoftware. .................. 103 5-2Theplotshowsthreetorqueterms ......................... 107 5-3Thetop-leftandbottom-leftplotsshowtheerrorsofLink1andLink2 ..... 110 5-4Thetop-leftandbottom-leftplotsshowthetorquesofLink1andLink2 .... 111 5-5TypicalinputdelayduringNMESinahealthyindividual. ............ 112 5-6Averageinputdelayvaluesacrossdierentfrequencies. .............. 113 5-7Averageinputdelayvaluesacrossdierentvoltages. ................ 114 5-8Averageinputdelayvaluesacrossdierentpulsewidths. .............. 115 5-9Topplot:Actuallimbtrajectoryofasubject(solidline)versusthedesiredtrajectory(dashedline)inputobtainedwiththePDcontrollerwithdelaycompensation. .. 117 5-10Topplot:Actuallimbtrajectoryofasubject(solidline)versusthedesiredtrajectory(dashedline)input .................................. 118 6-1Trackingerrorforthecase=3s: 132 6-2Controlinputforthecase=3s: 132 6-3Parameterestimatesforthecase=3s: 133 6-4Trackingerrorforthecase=10s: 133 6-5Controlinputforthecase=10s: 134 6-6Parameterestimatesforthecase=10s: 134 10

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Neuromuscularelectricalstimulation(NMES)istheapplicationofapotentialeldacrossamuscleinordertoproduceadesiredmusclecontraction.NMESisapromisingtreatmentthathasthepotentialtorestorefunctionaltasksinpersonswithmovementdisorders.Towardsthisgoal,theresearchobjectiveinthedissertationistodevelopNMEScontrollersthatwillenableaperson'slowershanktotrackacontinuousdesiredtrajectory(orconstantsetpoint). AnonlinearmusculoskeletalmodelisdevelopedinChapter 2 whichdescribesmuscleactivationandcontractiondynamicsandbodysegmentaldynamicsduringNMES.Thedenitionsofvariouscomponentsinthemusculoskeletaldynamicsareprovidedbutarenotrequiredforcontrolimplementation.Instead,thestructureoftherelationshipsisusedtodenepropertiesandmakeassumptionsforcontroldevelopment. AnonlinearcontrolmethodisdevelopedinChapter 3 tocontrolthehumanquadricepsfemorismuscleundergoingnon-isometriccontractions.Thedevelopedcontrollerdoesnotrequireamusclemodelandcanbeproventoyieldasymptoticstabilityforanonlinearmusclemodelinthepresenceofboundednonlineardisturbances.Theperformanceofthecontrollerisdemonstratedthroughaseriesofclosed-loopexperimentsonhealthynormalvolunteers.Theexperimentsillustratetheabilityofthecontrollertoenabletheshanktofollowtrajectorieswithdierentperiodsandrangesofmotion,andalsotrackdesiredstepchangeswithchangingloads. 11

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3 focusontheuseofaNNfeedforwardcontrollerthatisaugmentedwithacontinuousrobustfeedbacktermtoyieldanasymptoticresult(inlieuoftypicaluniformlyultimatelybounded(UUB)stability).Specically,aNN-basedcontrollerandLyapunov-basedstabilityanalysisareprovidedtoenablesemi-globalasymptotictrackingofadesiredtime-varyinglimbtrajectory(i.e.,non-isometriccontractions).TheaddedvalueofincorporatingaNNfeedforwardtermisillustratedthroughexperimentsonhealthynormalvolunteersthatcomparethedevelopedcontrollerwiththepureRISE-basedfeedbackcontroller. ApervasiveproblemwithcurrentNMEStechnologyistherapidonsetoftheunavoidablemusclefatigueduringNMES.Inclosed-loopNMEScontrol,disturbancessuchasmusclefatigueareoftentackledthroughhigh-gainfeedbackwhichcanoverstimulatethemusclewhichfurtherintensiesthefatigueonset.InChapter 4 ,aNMEScontrollerisdevelopedthatincorporatestheeectsofmusclefatiguethroughanuncertainfunctionofthecalciumdynamics.ANN-basedestimateofthefatiguemodelmismatchisincorporatedinanonlinearcontrollerthroughabacksteppingmethodtocontrolthehumanquadricepsfemorismuscleundergoingnon-isometriccontractions.ThedevelopedcontrollerisproventoyieldUUBstabilityforanuncertainnonlinearmusclemodelinthepresenceofboundednonlineardisturbances(e.g.,spasticity,delays,changingloaddynamics).Simulationsareprovidedtoillustratetheperformanceoftheproposedcontroller.ContinuedeortswillfocusonachievingasymptotictrackingversustheUUBresult,andonvalidatingthecontrollerthroughexperiments. AnotherimpedimentinNMEScontrolisthepresenceofinputoractuatordelay.Controlofnonlinearsystemswithactuatordelayisachallengingproblembecauseoftheneedtodevelopsomeformofpredictionofthenonlineardynamics.Theproblem 12

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5 foranEuler-Lagrangesystemwithtime-delayedactuation,parametricuncertainty,andadditiveboundeddisturbances.Onecontrollerisdevelopedundertheassumptionthattheinertiaisknown,andasecondcontrollerisdevelopedwhentheinertiaisunknown.Foreachcaseapredictor-likemethodisdevelopedtoaddressthetimedelayinthecontrolinput.Lyapunov-KrasovskiifunctionalsareusedwithinaLyapunov-basedstabilityanalysistoprovesemi-globalUUBtracking.ExtensiveexperimentsshowbetterperformancecomparedtotraditionalPD=PIDcontrolleraswellasrobustnesstouncertaintyintheinertiamatrixandtimedelayvalue.Experimentsareperformedonhealthynormalindividualstoshowthefeasibility,performance,androbustnessofthedevelopedcontroller. Inadditiontoeortsfocussedoninputdelayednonlinearsystems,aparallelmotivationexiststoaddressanotherclassoftimedelayedsystemswhichconsistofnonlinearsystemswithunknownstatedelays.AcontinuousrobustadaptivecontrolmethodisdesignedinChapter 6 foraclassofuncertainnonlinearsystemswithunknownconstanttime-delaysinthestates.Specically,therobustadaptivecontrolmethod,agradient-baseddesiredcompensationadaptationlaw(DCAL),andaLyapunov-Kravoskii(LK)functional-baseddelaycontroltermareutilizedtocompensateforunknowntime-delays,linearlyparameterizableuncertainties,andadditiveboundeddisturbancesforageneralnonlinearsystem.Despitethesedisturbances,aLyapunov-basedanalysisisusedtoconcludethatthesystemoutputasymptoticallytracksadesiredtimevaryingboundedtrajectory. Chapter 7 concludesthedissertationwithadiscussionofthedevelopedcontributionsandfutureeorts. 13

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1 2 ].AlthoughmostNMESproceduresinphysicaltherapyclinicsconsistoftabulatedopen-loopapplicationofelectricalstimulation,asignicantmarketexistsforthedevelopmentofnoninvasiveclosed-loopmethods.However,theapplicationanddevelopmentofNMEScontrolhavebeenstymiedbyseveraltechnicalchallenges.Specically,duetoavarietyofuncertaintiesinmusclephysiology(e.g.,temperature,pH,andarchitecture),predictingtheexactcontractionforceexertedbythemuscleisdicult.Onecauseofthisdicultyisthatthereisanunknownmappingbetweenthegeneratedmuscleforceandstimulationparameters.Thereareadditionalproblemswithdeliveringconsistentstimulationenergytothemuscleduetoavarietyoffactorsincluding:musclefatigue,inputdelay,electrodeplacement,hyperactivesomatosensoryreexes,inter-andintra-subjectvariabilityinmuscleproperties,changingmusclegeometryundertheelectrodesinnon-isometricconditions,percentageofsubcutaneousbodyfat,overallbodyhydration,etc. Giventheuncertaintiesinthestructureofthemusclemodelandtheparametricuncertaintyforspecicmuscles,someinvestigatorshaveexploredvariouslinearPID-basedmethods(cf.[ 3 { 8 ]andthereferencestherein).Typically,theseapproacheshaveonlybeenempiricallyinvestigatedandnoanalyticalstabilityanalysishasbeendevelopedthatprovidesanindicationoftheperformance,robustnessorstabilityofthesecontrolmethods.ThedevelopmentofastabilityanalysisforpreviousPID-basedNMEScontrollershasbeenevasivebecauseofthefactthatthegoverningequationsforamusclecontraction/limb 14

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6 9 10 ]);however,thegoverningequationsaretypicallylinearizedtoaccommodateagainschedulingorlinearoptimalcontrollerapproach. MotivatedtodevelopeectiveNMEScontrolinlightofthesechallenges,therstresultinChapter 3 developsanopen-looperrorsystemforageneraluncertainnonlinearmusclemodelbasedonavailableanalyticalandempiricaldata[ 11 12 ])thatfacilitatesthedevelopmentofanewcontinuousfeedbackmethod(coinedRISEforRobustIntegraloftheSignoftheError).Throughthiserror-systemdevelopment,thecontinuousRISEcontrollerisproven(throughaLyapunov-basedstabilityanalysis)toyieldanasymptoticstabilityresultdespitetheuncertainnonlinearmusclemodelandthepresenceofadditiveboundeddisturbances(e.g.,musclespasticity,fatigue,changingloadsinfunctionaltasks,andunmodeledmusclebehavior). Seminalworkin[ 13 { 18 ]continuetoinspirenewinvestigations(cf.[ 19 { 26 ]andthereferencestherein)inneuralnetwork(NN)-basedNMEScontroldevelopment.OnemotivationforNN-basedcontrollersisthedesiretoaugmentfeedbackmethodswithanadaptiveelementthatcanadjusttotheuncertainmusclemodel,ratherthanonlyrelyingonfeedbacktodominatetheuncertaintybasedonworsecasescenarios.NN-basedcontrolmethodshaveattractedmoreattentioninNMESthanotheradaptivefeedforwardmethodsbecauseofthenatureoftheunstructureduncertaintyandtheuniversalapproximationpropertyofNNs.However,sinceNNscanonlyapproximateafunctionwithinsomeresidualapproximationerror,allpreviousNN-basedcontrollersyielduniformlyultimatelyboundedstability(i.e.,theerrorsconvergetoaregionofboundedsteady-stateerror). TheresultinthethirdsectionofChapter 3 focusesonthedevelopmentofaRISE-basedNMEScontrollerandtheassociatedanalyticalstabilityanalysisthatyieldsasymptotictrackinginthepresenceofanonlinearuncertainmusclemodelwith 15

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27 ]indicatethattheRISE-basedfeedbackstructurecanbeaugmentedwithaNNfeedforwardtermtoyieldasymptotictrackingforsomeclassesofsystems.Basedonthesegeneralresults,anextensionisprovidedinthefourthsectionofChapter 3 wheretheRISE-basedmethodismodiedwithaNNtodevelopanewNMEScontrollerfortheuncertainmusclemodel. WhileeortsinChapter 3 ,provideaninroadtothedevelopmentofanalyticalNMEScontrollersforthenonlinearmusclemodel,theseresultsdonotaccountformusclefatigue,whichisaprimaryfactortoconsidertoyieldsomefunctionalresultsinmanyrehabilitationapplications.Heuristically,musclefatigueisadecreaseinthemuscleforceoutputforagiveninputandisacomplex,multifactorialphenomenon[ 28 { 30 ].Ingeneral,someofthefactorsassociatedwiththeonsetoffatiguearefailureofexcitationofmotorneurons,impairmentofactionpotentialpropagationinthemusclemembraneandconductivityofsarcoplasmicreticulumtoCa2+ionconcentration,andthechangeinconcentrationofcatabolitesandmetabolites[ 31 ].Factorssuchasthestimulationmethod,musclebrecomposition,stateoftrainingofthemuscle,andthedurationandtasktobeperformedhavebeennoticedtoaectfatigueduringNMES.GiventheimpactoffatigueeectsduringNMES,researchershaveproposeddierentstimulationstrategies[ 30 32 33 ]todelaytheonsetoffatiguesuchaschoosingdierentstimulationpatternsandparameters,improvingfatigueresistancethroughmuscleretraining,sequentialstimulation,andsizeorderrecruitment. Controllerscanbedesignedwithsomefeedforwardknowledgetoapproximatethefatigueonsetoremploysomeassumedmathematicalmodelofthefatigueinthecontroldesign.Researchersin[ 34 { 38 ]developedvariousmathematicalmodelsforfatigue.In 16

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34 ],amusculotendonmodelforaquadricepsmuscleundergoingisometriccontractionsduringfunctionalelectricalstimulation(FES)wasproposed.ThemodelincorporatedfatiguebasedontheintracellularpHlevelwherethefatigueparametersforatypicalsubjectwerefoundthroughmetabolicinformation,experimentationandcurvetting.Amoregeneralmathematicalmodelfordynamicfatiguedenedasafunctionofnormalizedmuscleactivationvariable(Ca2+dynamics)wasproposedin[ 35 36 ].Thefatiguewasintroducedasatnessfunctionthatvariesaccordingtotheincreaseordecreaseinmuscleactivationduringelectricalstimulation.Thefatiguetimeparameterswereestimatedfromstimulationexperiments.Modelsin[ 37 ]and[ 38 ]predictforceduetotheeectofstimulationpatternsandrestingtimeswithchangingphysiologicalconditions,wheremodelparameterizationrequiredinvestigatingexperimentalforcesgeneratedfromastandardizedstimulationprotocol.Althoughthesemathematicalmodelsforfatiguepredictionarepresentinliterature,fewresearchershaveutilizedtheseassumedfatiguemodelsinclosed-loopNMEScontrol.Resultsin[ 36 ]and[ 39 ]usethefatiguemodelproposedin[ 35 ]and[ 36 ]foraFEScontroller,wherepatientspecicparameters(e.g.fatiguetimeconstants)areassumedtobeknownalongwithexactmodelknowledgeofthecalciumdynamics.ThedicultyinvolvedinthecontroldesignusingcalciumdynamicsorintracellularpHlevelisthatthesestatescannotbemeasuredeasilyforreal-timecontrol.Therefore,thesestates(calciumdynamicsorpHlevel)aremodeledasarstorsecondorderordinarydierentialequation(cf.,[ 34 36 39 ])andtheparametersintheequationsareestimatedfromexperimentationorarebasedondatafrompaststudies. ThefocusofChapter 4 istoaddressmusclefatiguebyincorporatinganuncertainfatiguemodel(i.e.,themodeldevelopedin[ 35 ])intheNMEScontroller.Theuncertainfatiguemodelisdenedasafunctionofanormalizedmuscleactivationvariable.Thenormalizedmuscleactivationvariabledenotesthecalcium(Ca2+ion)dynamicswhichactasanintermediatevariablebetweencontractilemachineryandexternalstimulus.Thecalciumdynamicsaremodeledasarstorderdierentialequationbasedon[ 6 ]and 17

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39 ].AbacksteppingapproachisutilizedtodesignvirtualcontrolinputthatconsistsofNN-basedfeedforwardsignalandfeedbacksignal.Thedevelopedcontrolleryieldsauniformlyultimatelyboundedstabilityresultgivenanunknownnonlinearmusclemodelwithuncertainfatigueandcalciumdynamics. AnothertechnicalchallengethathampersthesatisfactoryNMEScontrolperformanceiselectromechanicaldelayinmuscleforcegenerationwhichisdenedasthedierenceintimefromthearrivalofactionpotentialattheneuromuscularjunctiontothedevelopmentoftensioninthemuscle[ 8 ].InNMEScontrol,theelectromechanicaldelayismodeledasaninputdelayinthemusculoskeletaldynamics[ 6 ]andoccursduetoniteconductionvelocitiesofthechemicalionsinthemuscleinresponsetotheexternalelectricalinput[ 36 ].InputdelaycancauseperformancedegradationaswasobservedduringNMESexperimentaltrialsonvolunteersubjectswithRISEandNN+RISEcontrollersandhasalsobeenreportedtopotentiallycauseinstabilityduringhumanstanceexperimentswithNMES[ 40 ].Timedelayinthecontrolinput(alsoknownasdeadtime,orinputdelay)isapervasiveproblemincontrolapplicationsotherthanNMEScontrol.Chemicalandcombustionprocesses,teleroboticsystems,vehicleplatoons,andcommunicationnetworks[ 41 { 44 ]oftenencounterdelaysinthecontrolinput.Suchdelaysareoftenattributedtosensormeasurementdelay,transportlags,communicationdelays,ortaskprioritization,andcanleadtopoorperformanceandpotentialinstability. Motivatedbyperformanceandstabilityproblems,variousmethodshavebeendevelopedforlinearsystemswithinputdelays(cf.[ 45 { 57 ]andthereferencestherein).Asdiscussedin[ 45 46 ],anoutcomeoftheseresultsisthedevelopmentanduseofpredictiontechniquessuchasArtsteinmodelreduction[ 48 ],nitespectrumassignment[ 51 ],andcontinuouspoleplacement[ 58 ].TheconceptofpredictivecontroloriginatedfromclassicSmithpredictormethods[ 59 ].TheSmithpredictorrequiresaplantmodelforoutputpredictionandhasbeenwidelystudiedandmodiedforcontrolpurposes(cf.[ 60 { 67 ]andreferencestherein).However,theSmithpredictordoesnotprovidegoodclosed-loop 18

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42 46 ].ContrarytotheSmithpredictor,nitespectrumassignmentorArtsteinmodelreductiontechniquesandtheirextensions[ 47 { 53 68 { 71 ]canbeappliedtounstableormultivariablelinearplants.Thesepredictor-basedmethodsutilizeniteintegralsoverpastcontrolvaluestoreducethedelayedsystemtoadelayfreesystem. Anotherapproachtodeveloppredictivecontrollersisbasedonthefactthatinputdelaysystemscanberepresentedbyhyperbolicpartialdierentialequations(cf.[ 45 46 ]andreferencestherein).Thisfactisexploitedin[ 54 { 57 ]todesigncontrollersforactuatordelayedlinearsystems.Thesenovelmethodsmodelthetimedelayedsystemasanordinarydierentialequation(ODE)-partialdierentialequation(PDE)cascadewherethenon-delayedinputactsatthePDEboundary.ThecontrolleristhendesignedbyemployingabacksteppingtypeapproachforPDEcontrol[ 72 ]. Predictortechniqueshavealsobeenextendedtoadaptivecontrolofunknownlinearplantsin[ 41 56 73 ].In[ 41 73 ]thecontrollerutilizesamodiedSmithpredictortypestructuretoachieveasemi-globalresult.In[ 56 ](andthecompanionpaper[ 55 ]),aglobaladaptivecontrollerisdevelopedthatcompensatesforuncertainplantparametersandapossiblylargeunknowndelay. Incomparisontoinputdelayedlinearsystems,fewerresultsareavailablefornonlinearsystems.Approachesforinputdelayednonlinearsystemssuchas[ 74 75 ]utilizeaSmithpredictor-basedgloballylinearizingcontrolmethodandrequireaknownnonlinearplantmodelfortimedelaycompensation.In[ 42 ],aspecictechniqueisdevelopedforateleroboticsystemwithconstantinputandfeedbackdelayswhereaSmithpredictorforalocallylinearizedsubsystemisusedincombinationwithaneuralnetworkcontrollerforaremotelylocateduncertainnonlinearplant.In[ 76 ],anapproachtoconstructLyapunov-Krasovskii(LK)functionalsforinputdelayednonlinearsysteminfeedbackformisprovided,andthecontrolmethodin[ 77 ]utilizesacompositeLyapunovfunctioncontaininganintegralcrosstermandLKfunctionalforstabilizingnonlinearcascade 19

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78 ]fornonlinearnite-dimensionalcontrolsystemsinpresenceofsmallinputdelaysbyutilizingaRazumikhin-typetheorem.In[ 79 ],thebacksteppingapproachthatutilizesODE-PDEcascadetransformationforinputdelayedsystemsisextendedtoascalarnonlinearsystemwithactuatordelayofunrestrictedlength.However,tothebestofourknowledge,noattempthasbeenmadetowardsstabilizinganinputdelayednonlinearsystemwithparametricuncertaintyand/oradditiveboundeddisturbances. MotivatedbythelackofNMEScontrollersthatcompensateforinputdelayandthedesiretodevelopnon-modelbasedcontrollersfornonlinearsystemswithinputdelayChapter 5 focusesonthedevelopmentofatrackingcontrollerforanuncertainnonlinearEuler-Lagrangesystemwithinputdelay.Theinputtimedelayisassumedtobeaknownconstantandcanbearbitrarylarge.Thedynamicsareassumedtocontainparametricuncertaintyandadditiveboundeddisturbances.Therstdevelopedcontrollerisbasedontheassumptionthatthemassinertiaisknown,whereasthesecondcontrollerisbasedontheassumptionthatthemassinertiaisunknown.Thekeycontributionsofthiseortisthedesignofadelaycompensatingauxiliarysignaltoobtainatimedelayfreeopen-looperrorsystemandtheconstructionofLKfunctionalstocanceltimedelayedterms.Theauxiliarysignalleadstothedevelopmentofapredictor-basedcontrollerthatcontainsaniteintegralofpastcontrolvalues.ThisdelayedstatetodelayfreetransformationisanalogoustotheArtsteinmodelreductionapproach,whereasimilarpredictor-basedcontrolisobtained.LKfunctionalscontainingniteintegralsofcontrolinputvaluesareusedinaLyapunov-basedanalysisthatprovesthetrackingerrorsaresemi-globaluniformlyultimatelybounded. Anotherclassoftime-delayedsystemswhicharealsoendemictoengineeringsystemsandcancausedegradedcontrolperformanceandmakeclosed-loopstabilizationdicultaresystemswithstatedelays.Intime-delayedsystems,thedynamicsnotonlydepends 20

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46 80 ].)AdesireparalleltoNMESresearchexistedtoaddressthisclassoftimedelaysystems.Variouscontrollershavebeendevelopedtoaddresstime-delayinducedperformanceandstabilityissuesasdescribedinthesurveypapers[ 45 46 ]andinrecentresultsthattargetcontrolofuncertainsystemswithstatedelays(cf.[ 80 { 86 ]andreferencestherein).Controlsynthesisandstabilityanalysismethodsfornonlineartime-delayedsystemsareoftenbasedonLyapunovtechniquesinconjunctionwithaLyapunov-Kravoskii(LK)functional(cf.[ 82 83 85 87 ]).Forexample,in[ 82 ],aniterativeprocedureutilizingLKfunctionalsforrobuststabilizationofaclassofnonlinearsystemswithtriangularstructureisdeveloped.However,asstatedin[ 88 ],thecontrollercannotbeconstructedfromthegiveniterativeprocedure.Semi-globaluniformlyultimatelybounded(SUUB)resultshavebeendevelopedfortime-delayednonlinearsystems[ 83 85 ]byutilizingneuralnetwork-basedcontrol,whereappropriateLKfunctionalsareutilizedtoremovetimedelayedstates.Adiscontinuousadaptivecontrollerwasrecentlydevelopedin[ 87 ]foranonlinearsystemwithanunknowntimedelaytoachieveaUUBresultwiththeaidofLKfunctionals.However,controllersdesignedin[ 83 87 ]canbecomesingularwhenthecontrolledstatereacheszeroandanadhoccontrolstrategyisproposedtoovercometheproblem.Moreover,asstatedin[ 89 ]and[ 90 ],thecontroldesignproceduredescribedin[ 85 ]cannotbegeneralizedfornthordernonlinearsystems. Slidingmodecontrol(SMC)hasalsobeenutilizedfortimedelayedsystemsin[ 80 91 { 94 ].However,utilizingSMCstillposesachallengingdesignandcomputationproblemwhendelaysarepresentinstates[ 45 46 ].Moreover,thediscontinuoussignfunctionpresentinSMCcontrolleroftengivesrisetotheundesirablechatteringphenomenonduringpracticalapplications.ToovercomethelimitationsofdiscontinuityinSMC,acontinuousadaptiveslidingmodestrategyisdesignedin[ 95 ]fornonlinearplantswith 21

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ThedevelopmentinChapter 6 ismotivatedbythelackofcontinuousrobustcontrollersthatcanachieveasymptoticstabilityforaclassofuncertaintime-delayednonlinearsystemswithadditiveboundeddisturbances.Theapproachdescribedinthecurrenteortusesacontinuousimplicitlearning[ 96 ]basedRobustIntegraloftheSignoftheError(RISE)structure[ 11 27 ].Duetotheaddedbenetofreducedcontroleortandimprovedcontrolperformance,anadaptivecontrollerinconjunctionwithRISEfeedbackstructureisdesigned.However,sincethetimedelayvalueisnotalwaysknown,itbecomeschallengingtodesignadelayfreeadaptivecontrollaw.Throughtheuseofadesiredcompensationadaptivelaw(DCAL)basedtechniqueandsegregatingtheappropriatetermsintheopenlooperrorsystem,thedependenceofparameterestimatelawsonthetimedelayedunknownregressionmatrixisremoved.Contrarytopreviousresults,thereisnosingularityinthedevelopedcontroller.ALyapunov-basedstabilityanalysisisprovidedthatusesanLKfunctionalalongwithYoung'sinequalitytoremovetimedelayedtermsandachievesasymptotictracking. 1. Chapter3,NonlinearNeuromuscularElectricalStimulationTrackingControlofaHumanLimb:Thecontributionofthischapteristoillustratehowarecentlydevelopedcontinuousfeedbackmethodcalledrobustintegralofsignumoftheerror(coinedasRISE)canbeappliedforNMESsystems.ThemusclemodeldevelopedinChapter 2 isrewritteninaformthatadherestoRISE-basedLyapunovstabilityanalysis.Throughthiserror-systemdevelopment,thecontinuousRISEcontrollerisproven(throughaLyapunov-basedstabilityanalysis)toyieldanasymptoticstabilityresultdespitetheuncertainnonlinearmusclemodelandthepresence 22

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ThesecondresultinthechapterfocussesonblendingNN-basedfeedforwardtechniquewithRISEbasedfeedbackmethodwhichwasshowntoyieldasymptotictrackinginthepresenceofanonlinearuncertainmusclemodelwithnonvanishingadditivedisturbances.Therstresultusesfeedbackandanimplicitlearningmechanismtodominateuncertaintyanddisturbances.Recentresultsfromgeneralcontrolsystemsliterature[ 27 ]indicatethattheRISE-basedfeedbackstructurecanbeaugmentedwithaNNfeedforwardtermtoyieldasymptotictrackingforsomeclassesofsystems.Basedonthesegeneralresults,theRISE-basedmethodismodiedwithamultilayeredNNtodevelopanewNMEScontrollerfortheuncertainmusclemodel.TheexperimentalresultsindicatethattheadditionoftheNNreducestherootmeansquared(RMS)trackingerrorforsimilarstimulationeortwhencomparedtotherstmethoddevelopedinthechapter(RISEmethodwithouttheNNfeedforwardcomponent).AdditionalexperimentswereconductedtodepictthattheNN-basedfeedforwardtechniqueholdspromiseinclinical-typetasks.Specically,apreliminarysit-to-standexperimentwasperformedtoshowcontroller'sfeasibilityforanyfunctionaltask. 2. Chapter4,NonlinearControlofNMES:IncorporatingFatigueandCalciumDy-namicsAnopen-looperrorsystemforanuncertainnonlinearmusclemodelisdevelopedthatincludesthefatigueandcalciumdynamics.AvirtualcontrolinputisdesignedusingnonlinearbacksteppingtechniquewhichiscomposedofaNNbasedfeedforwardsignalandanerrorbasedfeedbacksignal.TheNNbasedcontrolstructureisexploitednotonlytofeedforwardmuscledynamicsbutalsotoapproximatetheerrorgeneratedduetoparametricuncertaintiesintheassumedfatiguemodel.Theactualexternalcontrolinput(appliedvoltage)isdesignedbasedonthebacksteppingerror.Throughthiserror-systemdevelopment,thecontinuousNNbasedcontrollerisproven(throughaLyapunov-basedstabilityanalysis)toyieldanuniformlyultimatelyboundedstabilityresultdespitetheuncertainnonlinearmusclemodelandthepresenceofadditiveboundeddisturbances(e.g.,musclespasticity,changingloadsinfunctionaltasks,anddelays). 3. Chapter5,Predictor-BasedControlforanUncertainEuler-LagrangeSystemwithInputDelayThischapterfocusesonthedevelopmentofatrackingcontrollerfor 23

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4. Chapter6,RISE-BasedAdaptiveControlofanUncertainNonlinearSystemwithUnknownStateDelaysThedevelopmentinthischapterismotivatedbythelackofcontinuousrobustcontrollersthatcanachieveasymptoticstabilityforaclassofuncertaintime-delayednonlinearsystemswithadditiveboundeddisturbances.Theapproachdescribedinthecurrenteortusesacontinuousimplicitlearning[ 96 ]basedRobustIntegraloftheSignoftheError(RISE)structure[ 11 27 ].Duetotheaddedbenetofreducedcontroleortandimprovedcontrolperformance,anadaptivecontrollerinconjunctionwithRISEfeedbackstructureisdesigned.However,sincethetimedelayvalueisnotalwaysknown,itbecomeschallengingtodesignadelayfreeadaptivecontrollaw.Throughtheuseofadesiredcompensationadaptivelaw(DCAL)basedtechniqueandsegregatingtheappropriatetermsintheopenlooperrorsystem,thedependenceofparameterestimatelawsonthetimedelayedunknownregressionmatrixisremoved.Contrarytopreviousresults,thereisnosingularityinthedevelopedcontroller.ALyapunov-basedstabilityanalysisisprovidedthatusesanLKfunctionalalongwithYoung'sinequalitytoremovetimedelayedtermsandachievesasymptotictracking. 24

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Thefollowingmodeldevelopmentrepresentsthemusculoskeletaldynamicsduringneuromuscularelectricalstimulationperformedonhumanquadricepsmuscle.Themodelsimulateslimbdynamicswhenexternalvoltageisappliedonthemuscle.Thetotalmusclekneejointmodelcanbecategorizedintobodysegmentaldynamicsandmuscleactivationandcontractiondynamics.Themuscleactivationandcontractiondynamicsexplainstheforcegenerationinthemusclewhilethebodysegmentaldynamicsconsiderstheactivemomentandpassivejointmoments. Thetotalknee-jointdynamicscanbemodeledas[ 6 ] 2{1 ),MI(q)2Rdenotestheinertialeectsoftheshank-footcomplexaboutthe Figure2-1.Muscleactivationandlimbmodel.Theforcegeneratingcontractionandactivationdynamicsinthemuscleisdenotedbyanunknownnonlinearfunction(q;_q)2Rinthedynamics.ThedetailedcontractionandactivationdynamicsincludingfatigueandcalciumdynamicsareintroducedinChapter 4 25

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97 ],d(t)2Risconsideredasanunknownboundeddisturbancewhichrepresentsanunmodeledreexactivationofthemuscle(e.g.,musclespasticity)andotherunknownunmodeledphenomena(e.g.,dynamicfatigue,electromechanicaldelays),and(t)2Rdenotesthetorqueproducedatthekneejoint.Inthesubsequentdevelopment,theunknowndisturbanced(t)isassumedtobeboundedanditsrstandsecondtimederivativesareassumedtoexistandbebounded.Thesearereasonableassumptionsfortypicaldisturbancessuchasmusclespasticity,fatigue,andloadchangesduringfunctionaltasks.Forsimplicity,thepassivedampingandelasticforceofmuscleandjointsareconsideredtogether.Theinertialandgravitationaleectsin( 2{1 )canbemodeledas whereq(t),_q(t),q(t)2Rdenotetheangularposition,velocity,andaccelerationofthelowershankabouttheknee-joint,respectively(seeFig. 2-2 ),J2Rdenotestheunknowninertiaofthecombinedshankandfoot,m2Rdenotestheunknowncombinedmassoftheshankandfoot,l2Ristheunknowndistancebetweentheknee-jointandthelumpedcenterofmassoftheshankandfoot,andg2Rdenotesthegravitationalacceleration.TheelasticeectsaremodeledontheempiricalndingsbyFerrarinandPedottiin[ 97 ]as wherek1,k2,k32Rareunknownpositivecoecients.Asshownin[ 6 ],theviscousmomentMv(_q)canbemodelledas whereB1,B2,andB32Rareunknownpositiveconstants. 26

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ThetorqueproducedaboutthekneeiscontrolledthroughmuscleforcesthatareelicitedbyNMES.Forsimplicity(andwithoutlossofgenerality),thedevelopmentinthischapterfocusesonproducingkneetorquethroughmuscletendonforces,denotedbyFT(t)2R,generatedbyelectricalstimulationofthequadriceps(i.e.,antagonisticmuscleforcesarenotconsidered).Thekneetorqueisrelatedtothemuscletendonforceas where(q(t))2Rdenotesapositivemomentarmthatchangeswiththeextensionandexionofthelegasshowninstudiesby[ 98 ]and[ 99 ].ThetendonforceFT(t)in( 2{5 )isdenedas wherea(q(t))isdenedasthepennationanglebetweenthetendonandthemuscle.Thepennationangleofhumanquadricepsmusclechangesmonotonicallyduringquadricepscontractionandisacontinuouslydierentiable,positive,monotonic,andboundedfunctionwithaboundedrsttimederivative[ 100 ].Therelationshipbetweenmuscleforceandappliedvoltageisdenotedbytheunknownfunction(q;_q)2Ras 27

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101 102 ],orpulsewidth)iswellestablished.Theempiricaldatain[ 101 ]and[ 102 ]indicatesthefunction(q;_q)isacontinuouslydierentiable,non-zero,positive,monotonic,andboundedfunction,anditsrsttimederivativeisbounded. Thetotalforcegeneratedatthetendoncouldbeconsideredasthesumofforcesgeneratedbyanactiveelement(oftendenotedbyFCE),thetensiongeneratedbyapassiveelasticelement(oftendenotedbyFPE),andforcesgeneratedbyviscousuids(oftendenotedbyFVE).Theseforceshavedynamiccharacteristics.Forexample,thepassiveelementincreaseswithincreasingmusclelength,andthemusclestinesshasbeenreportedtochangebygreaterthantwoordersofmagnitude[ 34 ]underdynamiccontractions.Themusclemodelinthechapterconsidersthetotalmuscleforcecomposedofthesumoftheseelementsasthefunctionofanunknownnonlinearfunction(q;_q)andanappliedvoltageV(t):Theintroductionoftheunknownnonlinearfunction(q;_q)enablesthemusclecontractiontobeconsideredundergeneraldynamicconditionsinthesubsequentcontroldevelopment.Expressingthemusclecontractionforcesinthismannerenablesthedevelopmentofacontrolmethodthatisrobusttochangesintheforces,becausetheseeectsareincludedintheuncertainnonlinearmusclemodelthatisincorporatedinthestabilityanalysis.Themodeldevelopedin( 2{1 )-( 2{7 )isusedtoexaminethestabilityofthesubsequentlydevelopedcontroller,butthecontrollerdoesnotexplicitlydependonthesemodels.Thefollowingassumptionsareusedtofacilitatethesubsequentcontroldevelopmentandstabilityanalysis. 98 99 ]whosersttwotimederivativesexist,andbasedontheempiricaldata[ 101 102 ],thefunction(q;_q)isassumedtobeanon-zero,positive,andboundedfunctionwithaboundedrstandsecondtimederivatives. 28

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=cosa;(2{8) wheretherstandsecondtimederivativesof(q;_q)areassumedtoexistandbebounded(seeAssumption1). 29

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11 12 ])anditsextensionthroughcombiningNN-basedfeedforwardmethod.Throughthiserror-systemdevelopment,thecontinuousRISEcontrolleranditsmodicationisproven(throughaLyapunov-basedstabilityanalysis)toyieldanasymptoticstabilityresultdespitetheuncertainnonlinearmusclemodelandthepresenceofadditiveboundeddisturbances(e.g.,musclespasticity,fatigue,changingloadsinfunctionaltasks).Theperformanceofthetwononlinearcontrollersisexperimentallyveriedforhumanlegtrackingbyapplyingthecontrollerasavoltagepotentialacrossexternalelectrodesattachedtothedistal-medialandproximal-lateralportionofthequadricepsfemorismusclegroup.TheRISEandNN+RISEcontrollersareimplementedbyavoltagemodulationschemewithaxedfrequencyandaxedpulsewidth.Othermodulationstrategies(e.g.,frequencyorpulse-widthmodulation)couldhavealsobeenimplemented(andappliedtootherskeletalmusclegroups)withoutlossofgenerality. ThirdsectionofthechapterdiscussesthedevelopmentofRISEcontrollerforuncertainnonlinearmusclemodel.Theexperimentsillustratetheabilityofthecontrollertoenablethelegshanktotracksingleandmultipleperiodtrajectorieswithdierentperiodsandrangesofmotion,andalsotrackdesiredstepchangeswithchangingloads.InfourthsectiontheRISE-basedmethodismodiedwithaNNtodevelopanewNMEScontrollerfortheuncertainmusclemodel.TheexperimentalresultsindicatethattheadditionoftheNNreducestherootmeansquared(RMS)trackingerrorforsimilarstimulationeortwhencomparedtotherstresult(RISEmethodwithouttheNN 30

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Toquantifytheobjective,apositiontrackingerror,denotedbye1(t)2R,isdenedas whereqd(t)isanaprioritrajectorywhichisdesignedsuchthatqd(t),qid(t)2L1,whereqid(t)denotestheithderivativefori=1;2;3;4.Tofacilitatethesubsequentanalysis,lteredtrackingerrors,denotedbye2(t)andr(t)2R,aredenedas 31

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3{3 )byJandutilizingtheexpressionsin( 2{1 )and( 2{5 )-( 3{2 ),thefollowingexpressioncanbeobtained: whereW(_e1;e2;t)2Risanauxiliarysignaldenedas andthecontinuous,positive,monotonic,andboundedauxiliaryfunction(q;t)2Risdenedin( 2{8 ).Aftermultiplying( 3{4 )by1(q;t)2R,thefollowingexpressionisobtained: whereJ(q;t)2R;d(q;t)2R,andW(_e1;e2;t)2Raredenedas Tofacilitatethesubsequentstabilityanalysis,theopen-looperrorsystemfor( 3{6 )canbedeterminedas 2_Jr+N_Ve2;(3{9) whereN(e1;e2;r;t)2Rdenotestheunmeasurableauxiliaryterm 2_Jr+_d(q;t):(3{10) 32

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Afteraddingandsubtracting( 3{11 )to( 3{9 ),theopen-looperrorsystemcanbeexpressedas 2_Jr;(3{12) wheretheunmeasurableauxiliaryterm~N(e1;e2;r;t)2Risdenedas ~N(t)=NNd:(3{13) Motivationforexpressingtheopen-looperrorsystemasin( 3{12 )isgivenbythedesiretosegregatetheuncertainnonlinearitiesanddisturbancesfromthemodelintotermsthatareboundedbystate-dependentboundsandtermsthatareupperboundedbyconstants.Specically,theMeanValueTheoremcanbeappliedtoupperbound~N(e1;e2;r;t)bystate-dependenttermsas wherez(t)2R3isdenedas andtheboundingfunction(kzk)isapositive,globallyinvertible,nondecreasingfunction.Thefactthatqd(t),qid(t)2L18i=1;2;3;4canbeusedtoupperboundNd(qd;_qd;qd;...qd;t)as whereNdand_Nd2Rareknownpositiveconstants. 33

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2{1 )-( 2{7 ),theRISE-basedvoltagecontrolinputV(t)isdesignedas whereks2Rdenotespositiveconstantadjustablecontrolgain,and(t)2Risthegeneralizedsolutionto _(t)=(ks+1)2e2(t)+sgn(e2(t));(0)=0;(3{18) where2Rdenotespositiveconstantadjustablecontrolgain,andsgn()denotesthesignumfunction.Althoughthecontrolinputispresentintheopen-looperrorsystemin( 3{4 ),anextraderivativeisusedtodeveloptheopen-looperrorsystemin( 3{12 )tofacilitatethedesignoftheRISE-basedcontroller.Specically,thetime-derivativeoftheRISEinputin( 3{17 )lookslikeadiscontinuousslidingmodecontroller.Slidingmodecontrolisdesirablebecauseitisamethodthatcanbeusedtorejecttheadditiveboundeddisturbancespresentinthemuscledynamics(e.g.,musclespasticity,loadchanges,electromechanicaldelays)whilestillobtaininganasymptoticstabilityresult.Thedisadvantageofaslidingmodecontrolleristhatitisdiscontinuous.Bystructuringtheopen-looperrorsystemasin( 3{12 ),theRISEcontrollerin( 3{17 )canbeimplementedasacontinuouscontroller(i.e.,theuniqueintegralofthesignoftheerror)andstillyieldanasymptoticstabilityresult.Withoutlossofgenerality,thedevelopedvoltagecontrolinputcanbeimplementedthroughvariousmodulationmethods(i.e.,voltage,frequency,orpulsewidthmodulation). Theorem1. 3{17 )ensuresthatallsystemsignalsareboundedunderclosed-loopoperation.Thepositiontrackingerrorisregulatedinthesensethat 34

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3{17 )isselectedsucientlylarge,andisselectedaccordingtothefollowingsucientcondition: 3{16 ). 1 andtheauxiliaryfunctionP(t)2Risthegeneralizedsolutiontothedierentialequation _P(t)=L(t);P(0)=je2(0)je2(0)Nd(0):(3{22) TheauxiliaryfunctionL(t)2Rin( 3{22 )isdenedas ProvidedthesucientconditionsstatedinTheorem 1 aresatised,thenP(t)0. LetVL(y;t):D[0;1)!RdenoteaLipschitzcontinuousregularpositivedenitefunctionaldenedas 2eT2e2+1 2rTJr+P;(3{24) whichsatisfytheinequalities providedthesucientconditionintroducedTheorem 1 issatised,whereU1(y);U2(y)2Rarecontinuous,positivedenitefunctions.Aftertakingthetimederivativeof( 3{24 ),_VL(y;t)canbeexpressedas _VL(y;t),2e1_e1+1 2e2_e2+Jr_r+1 2_Jr2+_P:(3{26) 35

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3{2 ),( 3{3 ),( 3{12 ),( 3{22 ),and( 3{23 ),someofthedierentialequationsdescribingtheclosed-loopsystemforwhichthestabilityanalysisisbeingperformedhavediscontinuousright-handsidesas _e1=e21e1; _e2=r2e2; 2_Jr+~N+Nde2(ks+1)rsgn(e2); _P(t)=r(Nd(t)sgn(e2)): Letf(y;t)2R4denotetherighthandsideof( 3{27 ).Sincethesubsequentanalysisrequiresthatasolutionexistsfor_y=f(y;t),itisimportanttoshowtheexistenceofthesolutionto( 3{27 ).Asdescribedin[ 103 { 106 ],theexistenceofFilippov'sgeneralizedsolutioncanbeestablishedfor( 3{27 ).First,notethatf(y;t)iscontinuousexceptinthesetf(y;t)je2=0g.From[ 103 { 106 ],anabsolutecontinuousFilippovsolutiony(t)existsalmosteverywhere(a.e.)sothat _y2K[f](y;t)a:e:(3{28) Exceptthepointsonthediscontinuoussurfacef(y;t)je2=0g,theFilippovset-valuedmapincludesuniquesolution.UnderFilippov'sframework,ageneralizedLyapunovstabilitytheorycanbeused(see[ 106 { 109 ]forfurtherdetails)toestablishstrongstabilityoftheclosed-loopsystem.Thegeneralizedtimederivativeof( 3{24 )existsa.e.,and_VL(y;t)2a:e:~VL(y;t)where~VL=2@VL(y;t)TK_e1_e2_r1 2P1 2_P1T:=rVTLK_e1_e2_r1 2P1 2_P1T2e1e2rJ2P1 21 2_Jr2K_e1_e2_r1 2P1 2_P1T;

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107 ],andK[]isdenedas[ 108 109 ]K[f](y),\>0\N=0 3{2 ),( 3{3 ),( 3{12 ),( 3{17 ),( 3{18 ),( 3{22 ),and( 3{23 ) 2_Jr2+r~N+rNdre2(ks+1)r2rK[sgn(e2)]1 2_Jr2rNd(t)+rK[sgn(e2)]; where[ 108 ] suchthat Cancellingcommontermsandbasedonthefactthat 2e1e2ke2k2+ke1k2;(3{32) ( 3{29 )canbewrittenas Asshownin( 3{29 )-( 3{33 ),theuniqueintegralsignumtermintheRISEcontrollerisusedtocompensateforthedisturbancetermsincludedinNd(qd;_qd;qd;...qd;t),providedthecontrolgainisselectedaccordingto( 3{20 ).Using( 3{14 ),thetermr(t)~N(e1;e2;r;t),canbeupperboundedbyfollowinginequality: 37

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Completingthesquaresforthebracketedtermsin( 3{35 )yields Thefollowingexpressioncanbeobtainedfrom( 3{36 ): whereU(y)isacontinuouspositivedenitefunction,providedksisselectedsucientlylargebasedontheinitialconditionsofthesystem.Thatis,theregionofattractioncanbemadearbitrarilylargetoincludeanyinitialconditionsbyincreasingthecontrolgainks(i.e.,asemi-globaltypeofstabilityresult),andhence Basedonthedenitionofz(t)in( 3{15 ),( 3{38 )canbeusedtoshowthat 3{17 ).Thevoltagecontrollerwasimplementedthroughanamplitudemodulationschemecomposedofavariableamplitudepositivesquarewavewithaxedpulsewidthof100secandxedfrequencyof30Hz.The100secpulsewidthandthe30Hzstimulationfrequencywerechosena-prioriandrepresentparametricsettingsthatarewithintherangestypicallyreportedduringNMESstudies.Duringstimulationat100secpulsewidths,humanskeletalmuscleresponsetochangesinstimulationamplitude(force-amplituderelationship)andfrequency(force-frequencyrelationship)arehighlypredictableandthusdeemed 38

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110 ]whichshowthatasstimulationfrequencyisincreasedmuscleforceincreasestoasaturationlimit.Higherfrequenciescanbechosentogeneratemoreforceuptoasaturationlimit,butmusclestendtofatiguefasterathigherfrequencies.The30Hzpulsewaveyieldsreducedfatigueincomparisontohigherfrequenciesbutlowerfrequenciestendtoproducerippledkneemotion[ 35 110 ].Thereforestimulationfrequenciesintherangeof30-40Hzisanoptimalchoiceforconductingexternalelectricalstimulation.ThefollowingresultsindicatethattheRISEalgorithmwasabletominimizethekneeangleerrorwhiledynamicallytrackingadesiredtrajectory. Intheexperiment,bipolarself-adhesiveneuromuscularstimulationelectrodeswereplacedoverthedistal-medialandproximal-lateralportionofthequadricepsfemorismusclegroupandconnectedtothecustomstimulationcircuitry.Priortoparticipatinginthestudy,writteninformedconsentwasobtainedfromallthesubjects,asapprovedbytheInstitutionalReviewBoardattheUniversityofFlorida.Trackingexperimentsforatwoperioddesiredtrajectorywereconductedonbothlegsofvesubjects.Thesubjectsincludedtwohealthyfemalesandthreehealthymalesintheagegroupof22to26years.Theelectricalstimulationresponsesofhealthysubjectshavebeenreportedassimilartoparaplegicsubjects'responses[ 16 22 39 111 ].ThereforehealthysubjectswereusedinNMESexperimentsassubstituteforparaplegicpatientswhichwerenotavailable.AsdescribedinSection 3.3.2.2 ,theresultswereapproximatelyequalacrossthesubjects(i.e.,astandarddeviationof0.53degreesofRootMeanSquared(RMS)trackingerror). 39

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Duringtheexperimentseachsubjectwasinstructedtorelaxandtoallowthestimulationtocontrolthelimbmotion(i.e.,thesubjectswerenotsupposedtoinuencethelegmotionvoluntarilyandwerenotallowedtoobservethedesiredtrajectory).Varyingthetimeperiodandrangeofmotionmayalsohelptoreduceanypossibletrajectorylearningandanticipationbyahealthysubject.Toexperimentallyexamineifanytrajectorylearningoccurred,foursuccessivetestswereconductedonahealthysubjectwithatwominuteintervalbetweentrials.Theexperimentswereconductedfor15secondsonadualperiodtrajectoryof4and6seconds.TheresultingRMSerrorsaregiveninTable 3-1 .TheresultsinTable 3-1 illustratethattrajectorylearningbythesubjectisnotapparentsincethestandarddeviationbetweenthesuccessivetrialsis0:039degrees. RMSerror(indeg.) 4:35 2 4:28 3 4:26 4 4:29 Table3-1.TabulatedresultsindicatethatthetestsubjectwasnotlearningthedesiredtrajectorysincetheRMSerrorsarerelativelyequalforeachtrial. 3-1 ,aresummarizedinTable 3-2 .InTable 3-2 ,themaximumsteady-stateerrorisdenedasthemaximumabsolutevalueoferrorthatoccursafter4secondsofthetrial.Themaximumsteady-stateerrorsrangefrom4.25to7.55degreeswithameanof6.32degreesandastandarddeviationof1.18degrees.TheRMStrackingerrorsrangefrom2to3:47withameanRMSerrorof2.75degreesandastandarddeviationof0.53degrees.ThetrackingerrorresultsforSubjectBandthecorrespondingoutputvoltagescomputedbytheRISEmethod(priortovoltagemodulation)areshowninFig. 3-1 .The 40

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Leg RMSError Max.SteadyStateError Left 2:89 Right 2:36 Left 2:00 Right 2:35 Left 2:07 Right 2:94 Left 3:47 Right 2:89 Left 3:11 Right 3:45 2:75 0:53 Tofurtherillustratetheperformanceofthecontroller,experimentswerealsoconductedfortrajectorieswithfasterandslowerperiodsandlargerrangesofmotion.Specically,thecontroller'sperformancewastestedforadesiredtrajectorywithaconstant2secondperiod,aconstant6secondperiod,atripleperiodictrajectorywithcyclesof2,4;and6secondsandforahigherrangeofmotionof65degrees.AsindicatedinTable 3-1 ,theresultsforthetwoperiodtrajectoryyieldedsimilarresultsforallthesubjects.Hence,theseadditionaltestswereperformedonasingleindividualsimplytoillustratethecapabilitiesofthecontroller,withtheunderstandingthatsomevariationswouldbeapparentwhenimplementedondierentindividuals.TheRMStrackingerrorsandmaximumsteady-stateerrorsareprovidedinTable 3-3 .TheRMSerrorandthemaximumsteadystateerrorsarelowestforaconstant6secondperioddesiredtrajectoryandhigherforfastertrajectoriesandhigherrangeofmotion.Theseresultsareanexpectedoutcomesincetrackingmoreaggressivetrajectoriesgenerallyyieldsmoreerror.Thetripleperiodictrajectoryconsistsofamixofslowerandfasterperiod 41

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trajectories,thereforetheRMSandthemaximumsteadystateerrorsareinbetweentherespectiveerrorsobtainedformoreaggressive2secondperiodandhigherrangeofmotiondesiredtrajectories.Figs. 3-2 3-5 depicttheerrorsfortheexperimentssummarizedinTable 3-3 Additionalexperimentswerealsoconductedtoexaminetheperformanceofthecontrollerinresponsetostepchangesandchangingloads.Specically,adesiredtrajectoryofastepinputwascommandedwitha10poundloadattachedtotheLEM.Anadditional 42

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A B 2:88 6.13 Constant2sec. 4:11 10.67 Tripleperiodic(6;4;2)sec. 3:27 7.82 Tripleperiodic(6;4;2)secwithhigherrangeofmotion 5:46 12.48 Table3-3.Summarizedexperimentalresultsformultiple,higherfrequenciesandhigherrangeofmotion.Column(A)indicatesRMSerrorindegrees,andcolumn(B)indicatesmaximumsteadystateerrorindegrees. Figure3-2.Topplot:Actuallimbtrajectory(solidline)versusthedesiredtripleperiodictrajectory(dashedline).Bottomplot:Thelimbtrackingerror(desiredangleminusactualangle)ofasubjecttrackingatripleperiodicdesiredtrajectory. 10poundloadwasaddedoncethelimbstabilizedafterastepdownof15degrees.Thelimbwasagaincommandedtoperformastepresponsetoraisethelimbbackupanadditional15degreeswiththetotalloadof20pounds.TheresultsareshowninFig. 3-6 .Thesteadystateerrorwaswithin1degree.Amaximumerrorof3degreeswasobservedwhentheexternalloadwasadded.Theresultsgivesomeindicationofthecontroller's 43

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abilitytoadapttochangesinloadandstepinputsandmotivatepossiblefuturecasestudieswithneurologicallyimpairedindividualsthatexpressmusclespasticity. Foreachexperiment,thecomputedvoltageinputwasmodulatedbyaxedpulsewidthof100secandxedfrequencyof30Hz.Thestimulationfrequencywasselectedbasedonsubjectcomfortandtominimizefatigue.Duringpreliminaryexperimentswithstimulationfrequenciesof100Hz,thesubjectsfatiguedapproximatelytwotimesfasterthaninthecurrentresults.Theresultsalsoindicatethata100secpulsewidthwasacceptable,thoughfuturestudieswillinvestigatehigherpulsewidthsintherangeof300350secwhichrecruitmoreslowfatiguingmotorunits[ 110 ].Ourpreviouspreliminaryexperimentsindicatedthatlongerpulsewidths(e.g.,1msec)producedsimilareectsasadirectcurrentvoltage. 44

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TheuseoftheRISEcontrolstructureismotivatedbyitsimplicitlearningcharacteristics[ 96 ]anditsabilitytocompensateforadditivesystemdisturbancesandparametricuncertaintiesinthesystem.TheadvantageoftheRISEcontrolleristhatitdoesnotrequiremusclemodelknowledgeandguaranteesasymptoticstabilityofthenonlinearsystem.Theexperimentalresultsindicatethatthisfeedbackmethodmayhavepromiseinsomeclinicalapplications. AlthoughtheRISEcontrollerwassuccessfullyimplemented,theperformanceofthecontrollermaybeimprovedbyincludingafeedforwardcontrolstructuresuchasneuralnetworks(ablackboxfunctionapproximationtechnique)orphysiological/phenomenologicalmusclemodels.SincetheRISEcontrollerisahighgainfeedbackcontrollerthatyieldsasymptoticperformance,addingafeedforwardcontrolelementmayimprovetransientand 45

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steadystateperformanceandreducetheoverallcontroleort,therebyreducingmusclefatigue.Anotherpossibleimprovementtothecontrolleristoaccountforfatigue.Fatiguecanbereducedforshortdurationsbyselectingoptimalstimulationparameters,butfunctionalelectricalstimulation(FES)mayrequireacontrollerthatadaptswithfatiguetoyieldperformancegainsforlongertimedurations.Thereforeourfuturegoalwillbetoincludeafatiguemodelinthesystemtoenhancethecontrollerperformance. 46

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changes,andchangesintheload.Specically,theexperimentalresultsindicatedthatwithnomusclemodel(andonlyvoltageamplitudemodulation),theRISEalgorithmcoulddeterminetheappropriatestimulationvoltageforthetrackingobjective.Forthefastesttestedtrajectorythemaximumsteady-statetrackingerrorswereapproximately10degrees,whereasthemaximumsteady-stateerrorinslowertrajectorieswereaslittleasapproximately4degrees.Anadvantageofthiscontrolleristhatitcanbeappliedwithoutknowledgeofpatientspecicparameterslikelimbmassorinertia,limbcenterofgravitylocation,parametersthatmodelpassiveandelasticforceelements.Thus,itsapplicationwouldnotrequirespecicexpertiseorextensivetestingpriortouse.Thecontroldevelopmentalsoaccountsforunmodeleddisturbance(e.g.musclespasticity)thatarecommonlyobservedinclinicalpopulations.Theproposedstrategyholdspromisefor 47

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2{1 ).Duetotheuniversalapproximationproperty,NN-basedestimationmethodscanbeusedtorepresenttheunknownnonlinearmusclemodelbyathree-layerNNas[ 112 ] 48

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3{40 ),U2R(N1+1)N2andW2R(N2+1)nareboundedconstantidealweightmatricesfortherst-to-secondandsecond-to-thirdlayersrespectively,whereN1isthenumberofneuronsintheinputlayer,N2isthenumberofneuronsinthehiddenlayer,andnisthenumberofneuronsintheoutputlayer.Thesigmoidactivationfunctionin( 3{40 )isdenotedby():RN1+1!RN2+1;and(x):RN1+1!Rnisthefunctionalreconstructionerror.Theadditionalterm"1"intheinputvectorx(t)andactivationterm()allowsforthresholdstobeincludedastherstcolumnsoftheweightmatrices[ 112 ].Thus,anytuningofWandUthenincludestuningofthresholds.Basedon( 3{40 ),thetypicalthreelayerNNapproximationforf(x)isgivenas[ 112 ] ^f(x)=^WT(^UTx);(3{41) where^U2R(N1+1)N2and^W2R(N2+1)naresubsequentlydesignedestimatesoftheidealweightmatrices.Theestimatemismatchfortheidealweightmatrices,denotedby~U(t)2R(N1+1)N2and~W(t)2R(N2+1)n,aredenedas ~U=U^U;~W=W^W;(3{42) andthemismatchforthehidden-layeroutputerrorforagivenx(t),denotedby~(x)2RN2+1,isdenedas ~=^=(UTx)(^UTx):(3{43) TheNNestimatehasceratinpropertiesandassumptionsthatfacilitatethesubsequentdevelopment. 112 ] 49

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3{44 )into( 3{43 )thefollowingexpressioncanbeobtained: ~=^0~UTy+O(~UTy)2;(3{45) where^0=0(^UTy): wherekkFistheFrobeniusnormofamatrix,tr()isthetraceofamatrix.TheidealweightsinaNNarebounded,butknowledgeofthisboundisanon-standardassumptionintypicalNNliterature(althoughthisassumptionisalsousedintextbookssuchas[ 112 113 ]).Iftheidealweightsareconstrainedtostaywithinsomepredenedthreshold,thenthefunctionreconstructionerrorwillbelarger.Typically,thiswouldyieldalargerultimatesteady-statebound.Yet,inthecurrentresult,themismatchresultingfromlimitingthemagnitudeoftheweightsiscompensatedthroughtheRISEfeedbackstructure(i.e.,theRISEstructureeliminatesthedisturbanceduetothefunctionreconstructionerror). 3{3 )byJandbyutilizingtheexpressionsin( 2{1 )and( 2{5 ){( 3{2 )as 50

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2{8 ).Thedynamicsin( 3{48 )canberewrittenas wheretheauxiliaryfunctionsfd(qd;_qd;qd)2RandS(q;qd;_q;_qd;qd)2Raredenedas andJ(q;_q)2R;L(q;_q)2R,andd(q;t)2Raredenedas Theexpressionin( 3{50 )canberepresentedbyathree-layerNNas wherexd(t)2R4isdenedasxd(t)=[1qd(t)_qd(t)qd(t)].Basedontheassumptionthatthedesiredtrajectoryisbounded,thefollowinginequalitieshold whereb1;b2andb32Rareknownpositiveconstants. 51

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3{49 )andthesubsequentstabilityanalysis,thecontroltorqueinputisdesignedas[ 27 ] where^fd(t)2Risthethree-layerNNfeedforwardestimatedesignedas ^fd=^WT(^UTxd)(3{55) and(t)2RistheRISEfeedbacktermdesignedas[ 11 96 114 115 ] TheestimatesfortheNNweightsin( 3{55 )aregeneratedon-lineusingaprojectionalgorithmas where12R(N2+1)(N2+1)and22R44areconstant,positivedenite,symmetricgainmatrices.In( 3{56 ),ks2Rdenotespositiveconstantadjustablecontrolgain,and(t)2Risthegeneralizedsolutionto _(t)=(ks+1)2e2(t)+1sgn(e2(t));(0)=0;(3{58) where12Rdenotespositiveconstantadjustablecontrolgain,andsgn()denotesthesignumfunction.Theclosed-looptrackingerrorsystemcanbedevelopedbysubstituting( 3{54 )into( 3{49 )as where ~fd(xd)=fd^fd:(3{60) 52

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3{59 )canbedeterminedas AlthoughthevoltagecontrolinputV(t)ispresentintheopenlooperrorsystemin( 3{49 ),anadditionalderivativeistakentofacilitatethedesignoftheRISE-basedfeedbackcontroller.Aftersubstitutingthetimederivativeof( 3{60 )into( 3{61 )byusing( 3{52 )and( 3{55 ),theclosedloopsystemcanbeexpressedasJ_r=_Jr+WT0(UTxd)UT_xd^WT(^UTxd)^WT0(^UTxd)^UT_xd^WT0(^UTxd)^UTxd+_(xd)+_S_+_d; where0(^UTx)=d(UTx)=d(UTx)jUTx=^UTx:AfteraddingandsubtractingthetermsWT^0^VT_xd+^WT^0~VT_xdto( 3{62 ),thefollowingexpressioncanbeobtained:J_r=_Jr+^WT^0~VT_xd+~WT^0^VT_xd^WT^0~VT_xdWT^0^VT_xd 3{43 ).UsingtheNNweighttuninglawsdescribedin( 3{57 ),theexpressionin( 3{63 )canberewrittenas 2_Jr+~N+Ne2(ks+1)rsgn(e2);(3{64) wheretheunmeasurableauxiliaryterms~N(e1;e2;r;t)andN(^W;^U;xd;t)2Rgivenin( 3{64 )aredenedas ~N(t)=1 2_Jr+_S+e2proj1^0^UT_xdeT2T^^WT^0proj2_xd^0T^We2TTxd(3{65) 53

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3{66 ),Nd(q;_q;xd;_xd;t)2Risdenedas whileNB(^W;^U;xd;_xd;t)2Risdenedas whereNB1(^W;^U;xd;_xd;t)andNB2(^W;^U;xd;_xd;t)2Raredenedas and Motivationforthedenitionsin( 3{65 )-( 3{67 )arebasedontheneedtosegregatetermsthatareboundedbystate-dependentboundsandtermsthatareupperboundedbyconstantsforthedevelopmentoftheNNweightupdatelawsandthesubsequentstabilityanalysis.Theauxiliarytermin( 3{68 )isfurthersegregatedtodevelopgainconditionsinthestabilityanalysis.Basedonthesegregationoftermsin( 3{65 ),theMeanValueTheoremcanbeappliedtoupperbound~N(e1;e2;r;t)as wherez(t)2R3isdenedas andtheboundingfunction(kzk)2Risapositivegloballyinvertiblenondecreasingfunction.BasedonAssumption3inChapter 2 ,( 3{46 ),( 3{47 ),( 3{53 ),and( 3{68 )-( 3{70 ),thefollowinginequalitiescanbedeveloped[ 27 ]:kNdk1kNBk2_Nd3

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Theorem2. 3{54 )-( 3{58 )ensuresthatallsystemsignalsareboundedunderclosed-loopoperationandthatthepositiontrackingerrorisregulatedinthesensethat 3{56 )and( 3{58 )areselectedsucientlylarge. 2 wheretheauxiliaryfunctionQ(t)2Risdenedas andP(t)2Risthegeneralizedsolutiontothedierentialequation _P(t)=L(t);P(0)=1je2(0)je2(0)N(0):(3{77) Since1and2in( 3{76 )areconstant,symmetric,andpositivedenitematrices,and2>0;itisstraightforwardthatQ(t)0:TheauxiliaryfunctionL(t)2Rin( 3{77 )isdenedas where1;22Rintroducedin( 3{58 )and( 3{78 )respectively,arepositiveconstantschosenaccordingtothefollowingsucientconditions 55

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3{73 ).Providedthesucientconditionsin( 3{79 )aresatised,thenP(t)0. LetVL(y;t):D[0;1)!RdenoteaLipschitzcontinuousregularpositivedenitefunctionaldenedas 2e22+1 2Jr2+P+Q;(3{80) whichsatisestheinequalities providedthesucientconditionsin( 3{79 )aresatised,whereU1(y);U2(y)2Rarecontinuous,positivedenitefunctionsdenedas where1;22Rareknownpositivefunctionsorconstants.From( 3{2 ),( 3{3 ),( 3{64 ),( 3{77 ),( 3{78 ),andaftertakingthetimederivativeof( 3{76 ),someofthedierentialequationsdescribingtheclosed-loopsystemforwhichthestabilityanalysisisbeingperformedhavediscontinuousright-handsidesas _e1=e21e1; _e2=r2e2; 2_Jr+~N+Ne2(ks+1)rsgn(e2); _P(t)=r(NB1(t)+Nd(t)1sgn(e2))_e2NB2(t)+2e2(t)2; _Q(t)=tr2~WT11~W+tr2~UT12~U: Letf(y;t)2R5denotetherighthandsideof( 3{83 ).f(y;t)iscontinuousexceptinthesetf(y;t)je2=0g.From[ 103 { 106 ],anabsolutecontinuousFilippovsolutiony(t)existsalmosteverywhere(a.e.)sothat_y2K[f](y;t)a:e:

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3{80 )existsa.e.,and_VL(y;t)2a:e:~VL(y;t)where 2P1 2_P1 2Q1 2_Q1T =rVTLK_e1_e2_r1 2P1 2_P1 2Q1 2_Q1T;2e1e2rJ2P1 22Q1 21 2_Jr2K_e1_e2_r1 2P1 2_P1 2Q1 2_Q1T: 3{83 to 3{84 anddiscussion,seeSection 3.3.1 .Afterutilizing( 3{2 ),( 3{3 ),( 3{64 ),( 3{77 ),( 3{78 ),theexpressionin 3{84 canberewrittenas 2_Jr2+r~N+rNre2(ks+1)r2rK[sgn(e2)]1 2_Jr2rNB1rNd(t)+rK[sgn(e2)]_e2NB2(t)+2e22+tr2~WT11~W+tr2~UT12~U: Using( 3{57 ),( 3{66 ),( 3{68 ),( 3{70 ),cancellingcommonterms,andbasedonthefactthat2e1e2ke2k2+ke1k2; 3{85 )canbewrittenas Asshownin( 3{85 )-( 3{86 ),theuniqueintegralsignumtermintheRISEcontrollerisusedtocompensateforthedisturbancetermsincludedinNd(qd;_qd;qd;...qd;t)andNB1(^W;^U;xd;_xd;t);providedthecontrolgain1and2areselectedaccordingto( 3{79 ).FurtherthetermNB2(^W;^U;xd;_xd;t)ispartiallyrejectedbytheuniqueintegralsignumtermandpartiallycancelledbyadaptiveupdatelaw.Using( 3{71 ),theterm 57

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Completingthesquaresforthebracketedtermsin( 3{87 )yields Thefollowingexpressioncanbeobtainedfrom( 3{88 ): whereU(y)=ckzk2,forsomepositiveconstantc2R,isacontinuouspositivesemi-denitefunctionthatisdenedonthefollowingdomain:D4=ny2R5jkyk12p whereSDisintroducedinTheorem 2 .Theregionofattractionin( 3{90 )canbemadearbitrarilylargetoincludeanyinitialconditionsbyincreasingthecontrolgainks(i.e.,asemi-globaltypeofstabilityresult),andhence Basedonthedenitionofz(t)in( 3{72 ),( 3{91 )canbeusedtoshowthat 58

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3{54 )-( 3{58 )inexperimentswithvolunteersubjects.Theseresultswerecomparedwiththepreviousresultsin[ 116 ]thatusedtheRISEfeedbackstructurewithouttheNNfeedforwardterm.TheNMEScontrollerwasimplementedasanamplitudemodulatedvoltagecomposedofapositiverectangularpulsewithaxedwidthof400secandxedfrequencyof30Hz.TheapriorichosenstimulationparametersarewithintherangestypicallyreportedduringNMESstudies[ 110 116 ].Withoutlossofgenerality,thecontrollerisapplicabletodierentstimulationprotocols(i.e.,voltage,frequency,orpulsewidthmodulation).Thefollowingresultsindicatethatthedevelopedcontroller(henceforthdenotedasNN+RISE)wasabletominimizethekneeangleerrorwhiledynamicallytrackingadesiredtrajectory. Theobjectiveinonesetofexperimentswastoenablethekneeandlowerlegtofollowanangulartrajectory,whereas,theobjectiveofasecondsetofexperimentswastoregulatethekneeandlowerlegtoaconstantdesiredsetpoint.Anadditionalpreliminarytestwasalsoperformedtotestthecapabilityofthecontrollerforasit-to-standtask.Foreachsetofexperiments,bipolarself-adhesiveneuromuscularstimulationelectrodeswereplacedoverthedistal-medialandproximal-lateralportionofthequadricepsfemorismusclegroupofvolunteersandconnectedtocustomstimulationcircuitry.Theexperimentswereconductedonnon-impairedmaleandfemalesubjects(asinourpreviousstudyin[ 116 ])withagerangesof20to35years,withwritteninformedconsentasapprovedby 59

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16 22 39 111 ].Thevolunteerswereinstructedtorelaxasmuchaspossibleandtoallowthestimulationtocontrolthelimbmotion(i.e.,thesubjectwasnotsupposedtoinuencethelegmotionvoluntarilyandwasnotallowedtoseethedesiredtrajectory). TheNN+RISEcontrollerwasimplementedwithathreeinputlayerneurons,twenty-vehiddenlayerneurons,andoneoutputlayerneuron.Theneuralnetworkweightswereestimatedon-lineaccordingtotheadaptivealgorithmin( 3{57 ).Foreachexperiment,thecomputedvoltageinputwasmodulatedbyaxedpulsewidthof400secandxedfrequencyof30Hz.Thestimulationfrequencywasselectedbasedonsubjectcomfortandtominimizefatigue.Ninesubjects(8males,1female)wereincludedinthestudy.Thestudywasconductedfordierenttypesofdesiredtrajectoriesincluding:a1.5secondperiodictrajectory,adualperiodictrajectory(4-6second),andasteptrajectory.Forthe1.5secondperiodictrajectory,controllerswereimplementedonbothlegsoffoursubjects,whiletherestofthetestswereperformedononlyonelegoftheotherthreesubjectssincetheywerenotavailableforfurthertesting.Threesubjects(1male,1female(bothlegs);1male(oneleg))wereaskedtovolunteerforthedualperiodicdesiredtrajectorytestswhileregulationtestswereperformedononeofthelegsoftwosubjects.Eachsubjectparticipatedinonetrialpercriteria(e.g.,oneresultwasobtainedinasessionforagivendesiredtrajectory).Foreachsession,apre-trialtestwasperformedoneachvolunteertondtheappropriateinitialvoltageforthecontrollertoreducetheinitialtransienterror.Afterthepre-trialtest,theRISEcontrollerwasimplementedoneachsubjectforathirtyseconddurationanditsperformancewasrecorded.ArestperiodofveminuteswasprovidedbeforetheNN+RISEcontrollerwasimplementedforanadditionalthirtysecondduration. 60

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3-7 3-8 andaresummarizedinTable 3-4 .InTable 3-4 ,themaximumsteadystatevoltage(SSV)andmaximumsteadystateerror(SSE)aredenedasthecomputedvoltageandabsolutevalueoferrorrespectively,thatoccurafter1:5secondsofthetrial.Pairedonetailedt-tests(acrossthesubjectgroup)wereperformedwithalevelofsignicancesetat=0:05.Theresultsindicatethatthedevelopedcontrollerdemonstratestheabilityofthekneeangletotrackadesiredtrajectorywithamean(foreleventests)RMSerrorof2.92degreeswithameanmaximumsteadystateerrorof7.01degrees.CombiningtheNNwiththeRISEfeedbackstructurein[ 116 ]yields(statisticallysignicant)reducedmeanRMSerror 61

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forapproximatelythesameinputstimulus.ThemaximumsteadystatevoltagesfortheRISEandNN+RISEcontrollersrevealednostatisticaldierences.ToillustratethattheperformanceofNN+RISEcontroller(incomparisontotheRISEcontrolleralone)canbemoresignicantfordierentdesiredtrajectories,bothcontrollerswereimplementedonthreesubjects(2male,1female)withthecontrolobjectivetotrackadualperiodic(46second)desiredtrajectorywithahigherrangeofmotion.ThestimulationresultsfromtheRISEandtheNN+RISEcontrollersareshowninFigs. 3-9 and 3-10 andaresummarizedinTable 3-5 .InTable 3-5 ,themaximumSSVandSSEwereobservedafter4secondsofthetrial.TheresultsillustrateNN+RISEcontrolleryieldsreducedmeanRMSerror(acrossthegroup)andreducedmeanmaximumSSE(acrossthegroup)for 62

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Leg RMSError MaxSSE RMSVoltage[V] MaxSSV[V] RISE NNR RISE NNR RISE NNR RISE NNR Left 3:59 23:98 29:5 31 A Right 2:60 25:40 32:95 31:5 B Left 2:47 22:81 28:1 26:95 B Right 2:83 23:03 29:8 30:5 C Left 3:18 6:17 40:14 48:9 44:8 C Right 2:97 35:15 46:4 42:3 D Left 3:23 28:24 30 34:1 D Right 3:53 14:95 24:2 23:4 E Left 3:92 31:46 45 40:5 F Left 3:38 28:13 31:8 34:1 G Left 3:52 43:44 49:8 50 3:20 28:79 36:04 35:38 Std.Dev. 0:45 8:29 9:44 8:08 p-value 0:02 0:08 0:28 0:22 approximatelythesameinputstimulus.Pairedonetailedt-tests(acrossthesubjectgroup)wereperformedwithalevelofsignicancesetat=0:05.TheresultsshowthatthedierenceinmeanRMSerrorandmeanmaximumSSEwerestatisticallysignicant.ThePvalueforthemeanRMSerror(0:00043)andmeanmaximumSSE(0:0033)t-testobtainedinthecaseofdualperiodictrajectoryissmallerwhencomparedtothePvalues(0:02and0:08,respectively)obtainedforthe1.5secondtrajectory.ThisdierenceindicatestheincreasedroleoftheNNforslowertrajectories(wheretheadaptationgainscanbeincreased). Asin[ 117 ],additionalexperimentswerealsoconductedtoexaminetheperformanceoftheNN+RISEcontrollerinresponsetostepchangesandchangingloads.Specically,adesiredtrajectoryofastepinputwascommandedwitha10poundloadattachedtotheLEM.Anadditional10poundloadwasaddedoncethelimbstabilizedat15degrees.Thelimbwasagaincommandedtoperformastepresponsetoraisethelimbbackupan 63

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additional15degreeswiththetotalloadof20pounds.TheresultsfromarepresentativesubjectusingNN+RISEcontrollerareshowninFig. 3-11 .TheexperimentalresultsforthestepresponseandloadadditionaregiveninTable 3-6 .Theresultsgivesomeindicationofthecontroller'sabilitytoadapttochangesinloadandstepinputsandmotivatepossiblefuturecasestudies. ExperimentswerealsoperformedtotesttheNN+RISEcontrollerforasit-to-standtask.Thesetestswereconductedonahealthyindividualinitiallyseatedonachair(seeFig. 3-12 ).Thekneeanglewasmeasuredusingagoniometer(manufacturedbyBiometrics 64

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Ltd.)attachedtobothsidesofthesubject'sknee,wheretheinitialkneeangleissettozero(sittingposition).Thegoniometerwasinterfacedwiththecustomcomputercontrolledstimulationcircuitviaanangledisplayunit(ADU301).Theobjectivewastocontroltheangularkneetrajectorythatresultedinthevolunteerrisingfromaseatedposition,withanaldesiredangleof90(standingposition).Theerror,voltage,anddesiredversusactualkneeangleplotsareshowninFig. 3-13 .TheRMSerrorandvoltageduringthisexperimentwereobtainedas2:92and26:88V;respectively.Thenalsteadystateerrorreachedwithin0:5,themaximumtransienterrorwasobservedas8:23;

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Leg RMSError MaxSSE RMSVoltage[V] MaxSSV[V] RISE NNR RISE NNR RISE NNR RISE NNR Left 2:35 29:19 34:10 34:09 A Right 1:73 29:67 35:75 34:62 B Left 3:52 36:34 44:04 43:47 B Right 3:39 38:57 45:30 46:19 C Right 3:84 24:09 29:67 29:68 2:97 31:57 37:77 37:61 Std.Dev. 0:89 5:85 6:69 6:93 p-value 0:0033 0:43 Leg Max.SSE(afterstepinput) Max.Tran-sientError Max.Error(duringdis-turbance) Max.SSV(af-terstepinput)[Volts] Left 0:7 B Right 0:6 Table3-6.Experimentalresultsforstepresponseandchangingloads andthemaximumvoltagewasobtainedas35:1V:Thesignicanceofthesetestsistodepicttheapplicabilityofthecontrolleronclinicaltaskssuchassittostandmaneuvers.Althoughtheexperimentswereconductedonahealthyindividual,thesepreliminaryresultsshowthatthecontrollerholdspromisetoprovidesatisfactoryperformanceonpatientsinaclinical-typescenario. TheNN+RISEstructureismotivatedbythedesiretoblendaNN-basedfeedforwardmethodwithacontinuousfeedbackRISEstructuretoobtainasymptoticlimbtrackingdespiteanuncertainnonlinearmuscleresponse.TheabilityoftheneuralnetworkstolearnuncertainandunknownmuscledynamicsiscomplementedbytheabilityofRISEtocompensateforadditivesystemdisturbances(hyperactivesomatosensoryreexesthatmaybepresentinimpairedindividuals)andNNapproximationerror.AlthoughtheNN+RISEcontrollerwassuccessfullyimplementedandcomparedtoRISEcontrollerinthepresentwork,theperformanceofthecontrollermaybefurtherimprovedineortstoreducethe 66

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eectsofmusclefatigueinfuturestudies.Fatiguecanbereducedforshortdurationsbyselectingoptimalstimulationparameters,butfunctionalelectricalstimulation(FES)mayrequireacontrollerthatadaptswithfatiguetoyieldperformancegainsforlongertimedurations.Thereforeourfuturegoalwillbetoincludeafatiguemodelandincorporatingcalciumdynamicsinthemuscledynamicstoenhancethecontrollerperformance. 67

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116 ].However,severallimitationsexistintheexperimentalstudy.ThecontributionfromtheNNcomponentwasobservedtoincreasebuttheRISEcontributiondidnotdeclineproportionally.Apossiblereasonforthisobservationisthatthe1.5secondperioddesiredtrajectoryhasalargedesiredaccelerationqd(t),whichisaninputtotheNNthatcanleadtolargevoltageswingsduringthetransientstage.Toreducelargevoltagevariantsduringthetransientduetoqd(t),theupdatelawgainsarereducedincomparisontogainsthatcouldbeemployedduringlessaggressivetrajectories.Theexperimentalresultswithslowertrajectories(dualperiodic-4-6secondperiod)illustratethattheNNcomponentcanplayalargerroledependingonthetrajectory.Specically,thedualperiodictrajectoryresultsindicatethattheRMSerrorobtainedwiththeNN+RISEcontrollerislowerthantheRMSerrorobtainedwiththeRISEcontrollerwithalowerPvalue(0:00043)comparedtothePvalue(0:02)obtainedwiththe1.5secondperiodtrajectory. 68

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Sinceatrajectoryforaspecicfunctionaltaskwasnotprovided,thedesiredtrajectoryusedintherstsetofexperimentswassimplyselectedasacontinuoussinusoidwithaconstant1:5secondperiod.Thedesiredtrajectorywasarbitrarilyselected,buttheperiodofthesinusoidisinspiredbyatypicalwalkinggaittrajectory.Astheworktransitionstoapplicationswhereaspecicfunctionaltrajectoryisgenerated,thecontrol 69

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AnanalysisofRMSerrorsduringextensionandexionphaseofthelegmovementsacrossdierentsubjects,trajectories(1.5secondanddualperiodic),andbothcontrollersshowedthatthemeanRMSerrorismorewhenlegismovingupwards(extensionphase)comparedtoperiodswhenlegismovingdownwards(exionphase).At-testanalysisshowedthattheresultsarestatisticallysignicantwithpvaluesof0:00013and0:0014obtainedfromRISEandNN+RISEcontrollers,respectively.ThemeanRMSerrorsduringextensionphaseforRISEandNN+RISEcontrollerswere3:49and2:68,respectivelywhilemeanRMSerrorsduringexionphaseforRISEandNN+RISEcontrollerswere2:96and2:42;respectively.SummarizedRMSerrorsforbothphasesareshowninTable 3-7 .Anincreasederrorduringextensionphasecanbeattributedtohighercontroleortrequiredduringextension.Theperformanceduringtheextensionphasecanalsobeaggravatedbyincreasedtimedelayandmusclefatigueduetotherequirementforhighermuscleforcecomparedtotheexionphase.Thisanalysisindicatesapossibleneedforseparatecontrolstrategiesduringextensionandexionphaseofthelegmovement.Particularly,futureeortswillinvestigateahybridcontrolapproachforeachphaseofmotion. Currentlytheexperimentswereperformedonnon-impairedpersons.Infuturestudieswithimpairedindividuals,ouruntestedhypothesisisthattheaddedvalueoftheNNfeedforwardcomponentwillbeevenmorepronounced(andthatthecontrollerwillremainstable)asdisturbancesduetomorerapidfatigueandmoresensitivesomatosensoryreexesmaybepresentinimpairedindividuals.Todelaytheonsetoffatigue,dierentresearchershaveproposeddierentstimulationstrategies[ 32 33 118 ]suchaschoosingdierentstimulationpatternsandparameters.TheNMEScontrollerinthisstudywas 70

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Leg Trajectory RMSError(RISE) RMSError(NN+RISE) Extension Flexion Extension Flexion A Left Dualperiod Right Dualperiod Left Dualperiod Right Dualperiod Right Dualperiod Left 1.5second Right 1.5second Left 1.5second Right 1.5second Left 1.5second Right 1.5second 2:96 Left 1.5second Right 1.5second Left 1.5second Left 1.5second Right 1.5second 3:49 0:00013 0:0014 implementedusingconstantpulsewidthamplitudemodulationofthevoltage.However,thecontrollercanbeimplementedusingothermodulationschemessuchaspulsewidthandfrequencymodulationwithoutanyimplicationsonthestabilityanalysis,buttheeectsofusingfrequencymodulationorvaryingpulsetrains(e.g.apulsetraincontainingdoublets)remaintobeinvestigatedclinically. 71

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72

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35 ])intheNMEScontroller.Thecontributionofthemethodisthatonlybestguessestimatesofpatientspecicfatiguetimeconstantsandnaturalfrequencyofcalciumdynamicsarerequiredandthemismatchbetweentheestimatedparametersandactualparametersisincludedinastabilityanalysis.Thefatiguemodelisdenedasafunctionofanormalizedmuscleactivationvariable.Thenormalizedmuscleactivationvariabledenotesthecalcium(Ca2+ion)dynamicswhichactasanintermediatevariablebetweencontractilemachineryandexternalstimulus.Thecalciumdynamicsaremodeledasarstorderdierentialequationbasedon[ 6 ]and[ 39 ].Anopen-looperrorsystemforanuncertainnonlinearmusclemodelisdevelopedthatincludesthefatigueandcalciumdynamics.AvirtualcontrolinputisdesignedusingnonlinearbacksteppingtechniquewhichiscomposedofaNNbasedfeedforwardsignalandanerrorbasedfeedbacksignal.TheNNbasedcontrolstructureisexploitednotonlytofeedforwardmuscledynamicsbutalsotoapproximatetheerrorgeneratedduetoparametricuncertaintiesintheassumedfatiguemodel.Theactualexternalcontrolinput(appliedvoltage)isdesignedbasedonthebacksteppingerror.Throughthiserror-systemdevelopment,thecontinuousNNbasedcontrollerisproven(throughaLyapunov-basedstabilityanalysis)toyieldanuniformlyultimatelyboundedstabilityresultdespitetheuncertainnonlinearmusclemodelandthepresenceofadditiveboundeddisturbances(e.g.,musclespasticity,changingloadsinfunctionaltasks,anddelays). 2 ismodiedtoconsidercalciumandfatiguedynamicsduringneuromuscularelectricalstimulation.Theadditionaldynamics 73

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2 ThetorqueproducedaboutthekneeisgeneratedthroughmuscleforcesthatareelicitedbyNMES.TheactivemomentgeneratingforceatthekneejointisthetendonforceFT(t)2Rdenedas[ 119 ] wherea(q(t))2Risdenedasthepennationanglebetweenthetendonandthemuscle,whereq(t),_q(t)2Rdenotetheangularpositionandvelocityofthelowershankabouttheknee-joint,respectively(seeFig. 2-2 ).Thepennationangleofthehumanquadricepsmusclechangesmonotonicallyduringquadricepscontractionandisacontinuouslydierentiable,positive,monotonic,andboundedfunctionwithaboundedrsttimederivative[ 100 ].ThemuscleforceF(t)2Rin( 4{1 )isdenedas[ 36 ] whereFm2Risthemaximumisometricforcegeneratedbythemuscle.Theuncertainnonlinearfunctions1(q);2(q;_q)2Rin( 4{2 )areforce-lengthandforce-velocityrelationships,respectively,denedas[ 36 120 121 ] whereb,l(q)2Rin( 4{3 )denotetheunknownshapefactorandthenormalizedlengthwithrespecttotheoptimalmusclelength,respectively,andv(q;_q)2Risanunknownnon-negativenormalizedvelocitywithrespecttothemaximalcontractionvelocityofthemuscle,andc1;c2;c3;c4areunknown,bounded,positiveconstants. 74

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Thedenitionsin( 4{3 )and( 4{4 )arenotdirectlyusedinthecontroldevelopment.Instead,thestructureoftherelationshipsin( 4{3 )and( 4{4 )isusedtoconcludethat1(q)and2(q;_q)arecontinuouslydierentiable,non-zero,positive,monotonic,andboundedfunctions,withboundedrsttimederivatives.Themuscleforcein( 4{2 )iscoupledtotheactualexternalvoltagecontrolinputV(t)2Rthroughanintermediatenormalizedmuscleactivationvariablex(t)2R.Themuscleactivationvariableisgovernedbyfollowingdierentialequation[ 34 119 ] 2_x=wx+wsat[V(t)];(4{5) wherew2Ristheconstantnaturalfrequencyofthecalciumdynamics.Thefunctionsat[V(t)]2R(i.e.,recruitmentcurve)isdenotedbyapiecewiselinearfunctionas whereVmin2Ristheminimumvoltagerequiredtogeneratenoticeablemovementorforceproductioninamuscle,andVmax2Risthevoltageofthemuscleatwhichnoconsiderableincreaseinforceormovementisobserved.Basedon( 4{5 )and( 4{6 ),alineardierentialinequalitycanbedevelopedtoshowthatx(t)2[0;1]:Musclefatigueisincludedin( 4{2 )throughtheinvertible,positive,boundedfatiguefunction'(x)2Rthatisgeneratedfromtherstorderdierentialequation[ 35 36 ] _'=1 where'ministheunknownminimumfatigueconstantofthemuscle,andTf,Trareunknowntimeconstantsforfatigueandrecoveryinthemuscle,respectively. 75

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whereqd(t)isanaprioritrajectorywhichisdesignedsuchthatqd(t),qid(t)2L1,whereq(i)d(t)denotestheithderivativefori=1;2;3;4.Tofacilitatethesubsequentanalysis,alteredtrackingerror,denotedbyr(t);isdenedas where2Rdenotesapositiveconstant. 76

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4{9 ),multiplyingtheresultingexpressionbyJ,andthenutilizingtheexpressionsin( 4{1 ),( 4{2 ),( 2{1 ),( 2{5 )and( 4{8 )as wheretheauxiliaryfunction(q;_q)2Risdenedas Aftermultiplying( 4{10 )by1(q;_q)2R,thefollowingexpressionisobtained: whereJ(q;t);d(q;t),L(q;_q)2RaredenedasJ=1J;d=1d;L=1(Me+Mg+Mv): 4.2 ),(q;_q)iscontinuouslydierentiable,positive,monotonic,andbounded.Alsothefunction1(q;_q)isbounded.Thersttimederivativesof(q;_q)and1(q;_q)existandarebounded.TheinertiafunctionJispositivedeniteandcanbeupperandlowerboundedas wherea1;a22Raresomeknownpositiveconstants.Alsousingtheboundednessof(q;_q);_(q;_q);1(q;_q) wherej;2Raresomeknownpositiveconstants. 77

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4{7 )apositiveestimate^'(^x)isgeneratedas^'=1 ^Tf('min^')^x+1 ^Tr(1^')(1^x), (4{15)1^'(0)>0; 4{5 )as 2^x=^w^x+^wsat[V(t)];(4{16) where^w2Rdenotestheconstantbestguessestimateofnaturalfrequencyofcalciumdynamicsw:Theestimatedfunction^'(^x)isupperboundedbyapositiveconstant'2R.Specically,'canbedeterminedas '=^'(0)+1+^Tr Thealgorithmusedin( 4{15 )ensuresthat^'(^x)remainsstrictlypositive.Basedon( 4{6 )and( 4{16 ),alineardierentialinequalitycanbedevelopedtoshowthat^x(t)2[0;1]: 4{12 )toyield 2jre'~x'e^x^'^x;(4{18) wheretheauxiliaryfunctionS(q;_q;qd;e;r;^x)2Risdenedas 2jr+e~'^x(4{19) andtheerrorfunctions'e(x;^x);~'(^x);~x(t)2Raredenotedas ~'(^x)='(^x)^'(^x);(4{20) 78

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Since'(^x)and'(x)areboundedfunctions,theerrorfunction'e(t)canbeupperboundedas where'2Rissomeknownpositiveconstant.TheauxiliaryfunctionS(q;_q;qd;e;r;^x)canberepresentedbyathree-layerNNas wherey(t)2R7isdenedas and(y)isafunctionalreconstructionerrorthatisboundedbyaconstantas 4{18 ),abackstepping-basedapproachisusedtoinjectavirtualcontrolinputxd(t)2R(i.e.,desiredcalciumdynamics)as 2jre^'^x+^'xd^'xd:(4{27) Basedon( 4{27 ),thevirtualcontrolinputisdesignedasathreelayerNNfeedforwardtermplusafeedbacktermas whereks2Rdenotesapositiveconstantadjustablecontrolgain.ThefeedforwardNNcomponentin( 4{28 ),denotedby^S(t)2Risgeneratedas ^S=^WT(^UTy):(4{29) 79

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4{29 )aregeneratedon-lineusingprojectionalgorithmas[ 27 ] where12R(N2+1)(N2+1)and22R(N1+1)(N1+1)areconstant,positivedenite,symmetricgainmatrices.Theclosed-looptrackingerrorsystemcanbedevelopedbysubstituting( 4{28 )into( 4{27 )as 2jre+~S+d'~x'e^xksr^'ex;(4{31) where~S(y)2Risdenedas ~S(y)=S^S;(4{32) andex(t)2Risthebacksteppingerrordenedas TheclosedloopsystemcanbeexpressedasJ_r=1 2jre+WT(UTy)^WT(^UTy)+(y)+d'~x'e^xksr^'ex: AfteraddingandsubtractingthetermsWT^+^WT~to( 4{34 ),thefollowingexpressioncanbeobtained:J_r=1 2jre+~WT^+^WT~+~WT~+(y)+d'~x'e^xksr^'ex; wherethenotations^()and~()areintroducedin( 3{43 ).TheTaylorseriesapproximationdescribedin( 3{44 )and( 3{45 )cannowbeusedtorewrite( 4{35 )as 2jre+N+~WT^+^WT^0~UTyksr^'ex;(4{36) 80

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Basedon( 4{14 ),( 4{23 ),( 4{26 ),( 4{30 ),thefactthatx(t);^x(t)2[0;1],andtheassumptionthatdesiredtrajectoriesarebounded,thefollowinginequalitycanbedeveloped[ 122 ]: wherei2R,(i=1;2)areknownpositiveconstantsandz2R2isdenedas 4{33 )canbedeterminedbyusing( 4{16 )as _ex=^w Basedon( 4{6 )and( 4{40 ),andassumptionthatcontrolinputremainsbelowthesaturationvoltageVmax,thecontrolinput(Voltageinput)V(t)2RisdesignedasV(t)=(VmaxVmin)(^w wherek2Rdenotesapositiveconstantadjustablecontrolgain.Substituting( 4{41 )into( 4{40 ),yields _ex=^'rkex:(4{42) Theorem3. 4{28 )and( 4{41 )ensuresthatallsystemsignalsareboundedunderclosed-loopoperationandthatthepositiontrackingerrorisregulatedinthe

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4{9 ),( 4{49 ),( 4{50 )areselectedaccordingtothefollowingsucientcondition: 4{38 ). 2eTe+1 2rTJr+1 2eTxex+1 2tr(~WT11~W)+1 2tr(~UT12~U): Byusing( 4{13 )andtypicalNNproperties[ 112 ],VL(t)canbeupperandlowerboundedas where1;2;2Rareknownpositiveconstants,andX(t)2R3isdenedas Takingthetimederivativeof( 4{45 ),utilizing( 4{9 ),( 4{36 ),( 4{42 ),andcancelingsimilartermsyields_VL=eTe+rTNrTksr+rT~WT^+rT^WT^0~UTyeTxkexrT(j_J)rtr(~WT11~W)tr(~UT12~U): Using( 4{14 )and( 4{38 ),theexpressionin( 4{48 )canbeupperboundedas_VLe2ks1r2+2kzkjrj+[jrj1ks2r2]ke2x+rT~WT^+rT^WT^0~UTytr(~WT11^W)tr(~UT12^U); 82

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Completingthesquaresforthebracketedtermin( 4{49 )andusingtheupdatelawsin( 4{30 )yields _VL[min(1;ks1)2]kzk2ke2x+21 Theinequalityin( 4{46 )canbeusedtorewrite( 4{51 )as _VL 2VL+";(4{52) where"2Risapositiveconstantdenedas 2;(4{53) and2Risdenedas Thelineardierentialinequalityin( 4{52 )canbesolvedas 2t+"2 2ti:(4{55) Providedthesucientconditionin( 4{44 )issatised,theexpressionsin( 4{45 )and( 4{55 )indicatethate(t);r(t);ex(t);~W(t);~U(t)2L1.Giventhate(t);r(t);qd(t);_qd(t)2L1;( 4{8 )and( 4{9 )indicatethatq(t);_q(t)2L1:Since~W(t);~U(t)2L1;( 3{42 )andAssumption1( 3.4 )canbeusedtoconcludethat^W(t);^U(t)2L1:Basedon( 4{5 ),itcanbeshownthat^x(t)2[0;1]:Giventhatqd(t),e(t);r(t);q(t);_q(t);^x(t)2L1;theNNinputvectory(t)2L1from( 4{25 ):Sinceex(t);^x(t)2L1;( 4{33 )canbeusedtoshowthatxd(t)2L1:Giventhatr(t);^W(t);^U(t);xd(t)2L1,( 4{28 )and( 4{29 )indicatethat^S(t),^'1(t)2L1:Sincee(t);r(t);^W(t);~W(t);~U(t);ex(t)^'(t)2L1,( 4{36 )and( 4{38 )indicatethat_r(t)2L1:Asr(t);y(t);^W(t)2L1;theupdatelaws^W(t);^U(t)2L1:Since^'(t);^x(t)2L1,itcanbeshownthat^'(t)2L1:Giventhatthe^'(t);^'1(t);_r(t);

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6 36 123 ].TheRISEandtheproposedcontrolleraretestedfortwodierentdesiredtrajectories:1)slowtrajectorywith6secondperiod,2)fasttrajectorywith2secondperiod. Figure4-2.Topplotshowsthekneeangleerrorfora6secondperiodtrajectoryusingtheproposedcontroller.Middleplotshowsthepulsewidthcomputedbytheproposedcontroller.Bottomplotshowstheactuallegangle(dashedline)vsdesiredtrajectory(solidline). FromtheresultsshowninFigs. 4-2 4-8 ,itisclearthattheproposedcontrollertracksbothtimevaryingdesiredtrajectoriesbetterthantheRISEcontroller.Figs. 4-4 and 4-5 illustratetheperformanceoftheRISEcontrollerwhenimplementedonmuscledynamicswithoutincludingthefatiguedynamics.ThesteadystateerrorfromtheRISEcontrollerisbetween8fordesiredtrajectorywithperiod6seconds.ThesteadystateerrorinthecaseofRISEcontrollerincreasesto14whenfastertrajectorywithperiod2secondsisused.Fig. 4-6 depictsthatthecontrolperformancedegradeslaterintimewhenRISE 84

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controllerisimplementedonmuscledynamicswithfatiguemodelincluded.Theproposedcontrollerwasimplementedonthecompletemuscledynamicsthatincludedthefatiguedynamics.Figs. 4-2 4-3 and 4-7 showthatthesteadystateerrorinthecaseofproposedcontrollerremainswithin0:5forbothslowandfasttrajectories.Fig. 4-8 showshowthefatiguevariableevolveswithtimeasadeceasinginputgain.Theproposedcontrollerisabletocompensateforthedecreasingcontrolgain,andtheperformancedoesnotdegradeovertimeasshowninFig. 4-7 85

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performedtoproveuniformlyultimatelyboundedresultinthepresenceofboundeddisturbances(e.gmusclespasticity),parametricuncertainties.SimulationresultsclearlyillustratethattheproposedcontrollerperformsbetterintermsofreducederrorincomparisontotheRISEcontroller.However,theperformanceofthecontrolleronvolunteersorpatientsremainstobeseen.Thecontroller'sdependenceonaccelerationandmathematicalfatigueandcalciummodelshinderitsimplementationonvolunteers.Themathematicalcalciumandfatiguemodelswereincorporatedduetothefactthatthemeasurementofactualfatiguestateandcalciumvariableisdicult.Futureeortscanbemadetoincorporateanobserver-baseddesigninthecontrollerinordertoestimatethefatigueandcalciumstates. 86

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Figure4-6.RISEcontrollerwithfatigueinthedynamics:Topplotshowsthekneeangleerrorfora6secondperiodtrajectoryusingtheRISEcontroller.MiddleplotshowsthepulsewidthcomputedbytheRISEcontroller.Bottomplotshowstheactuallegangle(dashedline)vsdesiredtrajectory(solidline). 87

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Figure4-8.Fatiguevariable 88

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Theprimarymotiveofthisresearchistodevelopandimplementacontrollerthatcompensatesforelectromechanicaldelay(EMD)inNMES.ThelastsectionofthechapterfocussesoncharacterizingEMDduringNMES.ExperimentsresultsobtainedfromhealthyvolunteersareprovidedwhichdescribetheeectofstimulationparametersontheEMDduringNMES.Finally,aPDcontrollerwithanaugmentedpredictorcomponent 89

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In( 5{1 ),M(q)2Rnndenotesageneralizedinertiamatrix,Vm(q;_q)2Rnndenotesageneralizedcentripetal-Coriolismatrix,G(q)2Rndenotesageneralizedgravityvector,F(_q)2Rndenotesgeneralizedfriction,d(t)2Rndenotesanexogenousdisturbance(e.g.,unmodeledeects),u(t)2Rnrepresentsthegeneralizeddelayedinputcontrolvector,where2Risaconstanttimedelay,andq(t);_q(t);q(t)2Rndenotethegeneralizedstates.Thesubsequentdevelopmentisbasedontheassumptionsthatq(t)and_q(t)aremeasurable,Vm(q;_q);G(q);F(_q);d(t)areunknown,thetimedelayconstant2Risknown 5.3.2 ,M(q)isassumedtobeknowntoillustratethedevelopmentofaPID-likecontroller.InSection 5.3.3 ,thisassumptionisremovedandaPD-likecontrollerisdeveloped.Throughoutthepaper,atimedependentdelayedfunctionisdenotedasx(t)(orasx)andatimedependentfunction(withouttimedelay)isdenotedasx(t)(orasx):Thefollowingassumptionsareusedinthesubsequentdevelopment. 90

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wherem1;m22R+areknownconstantsandkkdenotesthestandardEuclideannorm. where1;22R+areknownconstants. 5.3.1Objective 5{1 )totrackadesiredtrajectory,denotedbyqd(t)2Rn.Toquantifytheobjective,apositiontrackingerror,denotedbye1(t)2Rn,isdenedas 91

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5{1 )toaninputdelayfreesystem,anauxiliarysignaldenotedbyr(t)2Rn;isalsodenedas where22R+denotesaconstant.Theauxiliarysignalr(t)isonlyintroducedtofacilitatethesubsequentanalysis,andisnotusedinthecontroldesignsincetheexpressionin( 5{6 )dependsontheunmeasurablegeneralizedstateq(t): 5{6 )byM(q)andutilizingtheexpressionsin( 5{1 ),( 5{4 ),and( 5{5 ),thetransformedopen-looptrackingerrorsystemcanbeexpressedinaninputdelayfreeformas Basedon( 5{7 )andthesubsequentstabilityanalysis,thecontrolinputu(t)2Rnisdesignedas whereka2R+isaknownconstantthatcanbeexpandedas tofacilitatethesubsequentstabilityanalysis,whereka1;ka22R+areknownconstants.Thecontrolleru(t)in( 5{8 )isaproportionalintegralderivative(PID)controllermodiedbyapredictorlikefeedbacktermfortimedelaycompensation.Althoughthecontrolinputu(t)ispresentintheopenlooperrorsystemin( 5{7 ),anadditionalderivativeistakentofacilitatethesubsequentstabilityanalysis.Thetimederivativeof( 5{7 )canbeexpressedas 2_M(q)r+N+_dkar;(5{10) 92

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2_M(q)r+M(q)...qd+_M(q)qd+_Vm(q;_q)_q+Vm(q;_q)q+_G(q)+_F(_q) (5{11) +(1+2)M(q)r12M(q)e221M(q)e122M(q)e2+1_M(q)_e1+2_M(q)e2(1+2)(uu)21M(q)_e1; 5{6 )isusedtowritethetimederivativeof( 5{8 )as_u=kar: 5{10 ),thefollowingexpressionisobtained: 2_M(q)r+~N+Se2kar;(5{12) wheretheauxiliaryfunctions~N(e1;e2;r;t)2RnandS(qd;_qd;qd;...qd;t)2Rnaredenedas ~N=NNd+e2;S=Nd+_d:(5{13) Sometermsintheclosed-loopdynamicsin( 5{12 )aresegregatedintoauxiliarytermsin( 5{13 )becauseofdierencesinhowthetermscanbeupperbounded.Forexample,Assumptions2;3and4,canbeusedtoupperboundS(qd;_qd;qd;...qd;t)as where"12R+isaknownconstantandtheMeanValueTheoremcanbeusedtoupperbound~N(e1;e2;r;t)as ~N1(kzk)kzk;(5{15) 93

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andtheboundingfunction1(kzk)2Risaknownpositivegloballyinvertiblenondecreasingfunction.In( 5{16 ),ez2Rnisdenedasez4=uu=Ztt_u()d; 5{8 )ensuressemi-globallyuniformlyultimatelybounded(SUUB)trackinginthesensethat 5{5 ),( 5{6 ),and( 5{8 ),respectivelyareselectedaccordingtothefollowingsucientconditions: 2;2>1+222 whereQ(t)2Risdenedas[ 45 76 ] where!2R+isaknownconstant.ApositivedeniteLyapunovfunctionalcandidateV(y;t):D[01)!Risdenedas 2eT2e2+1 2rTM(q)r+Q;(5{21) 94

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where1;22R+areknownconstantsdenedas 2min[m1;1];2=max[1 2m2;1];(5{23) wherem1andm2aredenedin( 5{2 ). Afterutilizing( 5{5 ),( 5{6 ),and( 5{12 )andcancellingthesimilarterms,thetimederivativeof( 5{21 )is _V=2eT1e221eT1e12eT2e2karTr+eT2M1(q)ez+rTS+rT~N+!k_uk2!Zttk_u()k2d;(5{24) wheretheLeibnizintegralrulewasappliedtodeterminethetimederivativeofQ(t)in( 5{20 )(seetheAppendix 7.2 ).Theexpressionin( 5{24 )canbeupperboundedbyusing( 5{3 ),( 5{14 )and( 5{15 )as _V(211)ke1k2(21)ke2k2kakrk2+2ke2kkezk +!k_uk2+"1krk+1(kzk)kzkkrk!Zttk_u()k2d: 5{25 )canbeupperboundedbyusingYoung'sinequality: where2R+isaknownconstant.Further,byusingtheCauchySchwarzinequality,thefollowingtermin( 5{26 )canbeupperboundedas 95

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2Rttk_u()k2din( 5{25 )yields _V(211)ke1k2(21)ke2k2kakrk2+2ke2kkezk+!k_uk2 +"1krk+1(kzk)kzkkrk! 2Zttk_u()k2d 2Zttk_u()k2d: 5{9 )andtheboundsgivenin( 5{26 )and( 5{27 ),theinequalityin( 5{28 )canbeupperboundedas _V(211)ke1k2(21222 2kezk2+"1krk+1(kzk)kzkkrkka2krk2ka1krk2 2Zttk_u()k2d: Aftercompletingthesquares,theinequalityin( 5{29 )canbeupperboundedas _V1kzk2 2Zttk_u()k2d+21(kzk) 4ka1kzk2+"21 where12R+isdenedas1=min(21222 2: 5{30 )canberewrittenas _V121(kzk) 4ka1kzk21 Usingthedenitionofz(t)in( 5{16 )andy(t)in( 5{19 ),theexpressionin( 5{31 )canbeexpressedas _V1kyk2121(kzk) 4ka1kezk2+"21 where1(kzk)2R+isdenedas1=min121(kzk) 4ka1;1

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5{22 ),theinequalityin( 5{32 )canbeupperboundedas _V1 ConsiderasetSdenedas InS,1(kzk)canbelowerboundedbyaconstant12R+as Basedon( 5{35 ),thelineardierentialequationin( 5{33 )canbesolvedas providedkzk112p 5{36 ),ifz(0)2Sthenkacanbechosenaccordingtothesucientconditionsin( 5{18 )(i.e.asemi-globalresult)toyieldtheresultin( 5{17 ).Basedondenitionofy(t),itcanbeconcludedthate1(t);e2(t);r(t)2L1inS.Giventhate1(t);e2(t);qd(t);_qd(t)2L1inS;( 5{4 )and( 5{5 )indicatethatq(t);_q(t)2L1inS:Sincer(t);e2(t);q(t);_q(t);_qd(t);qd(t)2L1inS,andu(t)u(t)=Rtt_u()d=kaRttr()d(byLeibnitz-Newtonformula)2L1inS,then( 5{6 )andAssumption3indicatethatq(t)2L1inS:Giventhatr(t);e2(t);q(t);_q(t);_qd(t)qd(t)2L1inS,( 5{7 )andAssumptions3and4indicatethatu(t)2L1inS. 97

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whereb2R+isaknownconstant.Tofacilitatethesubsequentstabilityanalysis,theerrorbetweenBandM1(q)isdenedby where(q)2Rnnsatisesthefollowinginequality where2R+denotesaknownconstant.Theopen-looptrackingerrorsystemcanbedevelopedbymultiplyingthetimederivativeof( 5{37 )byM(q)andutilizingtheexpressionsin( 5{1 ),( 5{4 ),and( 5{39 )toobtain Basedon( 5{41 )andthesubsequentstabilityanalysis,thecontrolinputu(t)2Rnisdesignedas wherekb2R+isaknowncontrolgainthatcanbeexpandedas tofacilitatethesubsequentanalysis,wherekb1;kb2;andkb32R+areknownconstants:AfteraddingandsubtractingtheauxiliarytermNd(qd;_qd;qd;t)2RndenedasNd=M(qd)qd+Vm(qd;_qd)_qd+G(qd)+F(_qd);

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5{37 )and( 5{42 ),theexpressionin( 5{41 )canberewrittenas 2_M(q)e2+~N+Se1kbe2kbM(q)[e2e2];(5{44) wheretheauxiliaryterms~N(e1;e2;t);N(e1;e2;t);S(qd;_qd;qd;t)2Rnaredenedas ~N=NNd;S=Nd+d;(5{45)N=1 2_M(q)e2+M(q)qd+Vm(q;_q)_q+G(q)+F(q)+M(q)e22M(q)e1+e1+M(q)BZttu()d; ~N2(kzk)kzk;kSk"2:(5{46) In( 5{46 ),"22R+isaknownconstant,theboundingfunction2(kzk)2Risapositivegloballyinvertiblenondecreasingfunction,andz2R3nisdenedas whereez2Rnisdenedasez=Zttu()d: 5{42 )ensuresSUUBtrackinginthesensethat 5{37 )and( 5{42 ),respectivelyareselectedaccordingtothesucientconditions: 5{2 ),( 5{38 ),and( 5{40 ),respectively,and;!2R+aresubsequentlydenedconstants.

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2m2:Experimentalresultsillustratetheperformance/robustnessofthedevelopedcontrollerwithrespecttothemismatchbe-tweenBandM1(q).Specically,resultsindicateaninsignicantamountofvariationintheperformanceevenwheneachelementofM1(q)isoverestimatedbyasmuchas100%.Dierentresultsmaybeobtainedfordierentsystems,buttheseresultsindicatethatthegainconditionisreasonable. whereP(t),Q(t)2RdenoteLKfunctionalsdenedas[ 45 ]P=!ZttZtsku()k2dds;Q=m2kb 2eT1e1+1 2eT2M(q)e2+P+Q;(5{51) andsatisesthefollowinginequalities where1;22R+aredenedin( 5{23 ). Takingthetimederivativeof( 5{51 )andusing( 5{37 )and( 5{44 )yields _V=eT1e1+eT1Bez+!kuk2+eT2hS+~Nkbe2kbM(q)(e2e2)i+m2kb wheretheLeibnizintegralrulewasappliedtodeterminethetimederivativeofP(t)(seetheAppendix 7.2 )andQ(t).Using( 5{2 ),( 5{38 ),and( 5{46 ),thetermsin( 5{53 )canbe 100

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_Vke1k2kbke2k2+m2kbke2k2+!kuk2+ke2k"2+ke2k2(kzk)kzk+bke1kkezk+m2kbke2kke2k+m2kb Thefollowingtermsin( 5{54 )canbeupperboundedbyutilizingYoung'sinequality: m2kbke2kke2km2kb 5{55 )canbeupperboundedas Afteraddingandsubtracting 2Rttku()k2dto( 5{54 ),andutilizing( 5{42 ),( 5{43 ),( 5{55 )and( 5{56 ),thefollowingexpressionisobtained: _V(b22 2)kezk2kb1ke2k2+2(kzk)kzkke2kkb2ke2k2+ke2k"2 2Zttku()k2d: Bycompletingthesquares,theinequalityin( 5{57 )canbeupperboundedas _V222(kzk) 4kb1kzk2 2Zttku()k2d+"22 where22R+isdenotedas2=minb22 2):

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5{58 )canrewrittenas _V222(kzk) 4kb1kzk2 22ZttZtsku()k2d+"22 Usingthedenitionsofz(t)in( 5{47 ),y(t)in( 5{50 ),andu(t)in( 5{42 ),theexpressionin( 5{59 )canbeexpressedas _V2kyk2222(kzk) 4kb1kezk2+"22 where2(kzk)2R+isdenedas2=min222(kzk) 4kb1;kb 2m2;1 2!2: 5{52 ),theinequalityin( 5{60 )canbewrittenas _V2 ConsiderasetSdenedas InS,2(kzk)canbelowerboundedbyaconstant22R+as Basedon( 5{63 ),thelineardierentialequationin( 5{61 )canbesolvedas providedkzk<122p 5{64 ),ifz(0)2Sthenkbcanbechosenaccordingtothesucientconditionsin( 5{49 )(i.e.asemi-globalresult)toyieldresultin( 5{48 ).Basedonthedenitionofy(t),itcanbeconcludedthate1(t);e2(t)2L1inS.Giventhate1(t);e2(t);qd(t);_qd(t)2L1inS;( 5{4 ),( 5{42 ),and( 5{37 )indicatethatq(t);_q(t);u2L1inS:

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5-1 .EachrobotlinkismountedonanNSKdirectdriveswitched Figure5-1.Experimentaltestbedconsitingofa2-linkrobot.Theinputdelayinthesystemwasarticiallyinsertedinthecontrolsoftware. reluctancemotor(240.0NmModelYS5240-GN001,and20.0NmModelYS2020-GN001,respectively).TheNSKmotorsarecontrolledthroughpowerelectronicsoperatingintorquecontrolmode.Rotorpositionsaremeasuredthroughmotorresolverwitharesolutionof614400pulses/revolution.ThecontrolalgorithmswereexecutedonaPentium2.8GHzPCoperatingunderQNX.Dataacquisitionandcontrolimplementationwereperformedatafrequencyof1.0kHzusingtheServoToGoI/Oboard.Inputdelaywasarticiallyinsertedinthesystemthroughthecontrolsoftware(i.e.,thecontrolcommandstothemotorsweredelayedbyavaluesetbytheuser).Thedevelopedcontrollersweretestedforvariousvaluesofinputdelayrangingfrom1msto200ms.Thedesiredlinktrajectoriesforlink1(qd1(t))andlink2(qd2(t))wereselectedas(indegrees):qd1(t)=qd2(t)=20:0sin(1:5t)(1exp(0:01t3)):

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5{8 )(PIDcontrollerwithdelaycompensation)andthecontrollerdevelopedin( 5{42 )(PDcontrollerwithdelaycompensation)werecomparedwithtraditionalPIDandPDcontrollers,respectively,inthepresenceofinputdelayinthesystem.Theinputdelayedtwolinkrobotdynamicsaremodeledas264u1u2375=264p1+2p3cos(q2)p2+p3cos(q2)p2+p3cos(q2)p2375264q1q2375+264p3sin(q2)_q2p3sin(q2)(_q1+_q2)p3sin(q2)_q10375264_q1_q2375+264fd100fd2375264_q1_q2375+264fs100fs2375264tanh(_q1)tanh(_q2)375; 104

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124 ]waslinearizedatseveraloperatingpointsandalinearcontrollerwasdesignedforeachpoint,andthegainswerechosenbyinterpolating,orschedulingthelinearcontrollers.In[ 125 ],aneuralnetworkisusedtotunethegainsofaPIDcontroller.In[ 126 ]ageneticalgorithmwasusedtonetunethegainsafterinitialguessweremadebythecontrollerdesigner.Theauthorsin[ 127 ]provideanextensivediscussionontheuseofextremumseekingfortuningthegainsofaPIDcontroller.Additionally,in[ 128 ],thetuningofaPIDcontrollerforrobotmanipulatorsisdiscussed. TheexperimentalresultsaresummarizedinTable 5-1 .Theerrorandtorqueplotsforthecasewhentheinputdelayis50ms(asarepresentativeexample)areshowninFigs. 5-3 5-4 .ThePDcontrollerwithdelaycompensationwasalsotestedtoobservethesensitivityoftheBgainmatrix,denedin( 5{37 ),wheretheinputdelaywasselectedas100ms.EachelementoftheBgainmatrixwasincremented/decrementedbyacertainpercentagefromtheinverseinertiamatrix(seeTable 5-2 ).ThepurposeofthissetofexperimentswastoshowthatthegainconditiondiscussedinRemark 1 isasucientbutnotanecessarycondition,andtoexploretheperformance/robustnessofthecontrollerin( 5{42 )giveninexactapproximationsoftheinertiamatrix.Thecontrollerexhibitednosignicantdegradation,evenwheneachelementoftheinertiamatrixisover-approximatedby100%.However,underestimatingtheinverseinertiamatrix(particularlywhendeviationfromtheinverseinertiamatrixwas75percent), 105

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5-3 wereconductedtoshowthatpromisingresultscanbeobtainedevenwhentheinputdelayvalueisnotexactlyknown;however,thetrackingerrorperformancedegradeswithincreasinginaccuracyindelayvalueapproximation(e.g.,inthecaseofPD+compensator,thetrackingerrorincreasessignicantlywhenthedelayvalueisoverestimatedby80%orgreater).Forthissetofexperimentstheinputdelaywaschosentobe100ms. TheexperimentalresultsclearlyshowthatthePID/PDcontrollerswithdelaycompensationperformbetterthanthetraditionalPID/PDcontrollers.BothcontrollerscanbedividedintorespectivePID/PDcomponentsandpredictor(delaycompensating)terms.Thebetterperformanceshownbythecontrollerscanbeattributedtothepredictorcomponentsinboththecontrollers.Asanillustrativeexample,Fig. 5-2 showsthetimeplotsofthePDcontrollerwithdelaycompensationanditscontrolcomponents.Thetwocomponents:PDcomponentanddelaycompensatingtermareplottedtoshowtheirbehaviorwithrespecttoeachother.TheplotshowsthatthedelaycompensatingcomponentisalwaysfollowingthePDcomponentbutisoppositeinsign(likeanmirrorimagebutlessinmagnitude).Thus,thenet(actual)controltorqueisalwayslessthanthePDcontrolcomponent.ThisimpliesthatthedelaycompensatingtermtendstocorrectthePDcomponent(actsasaprimarytorquegenerator)whichmayhavecompiledextraneoustorqueduetotheinputdelay.Thedelaycompensatingtermpredictsthecorrectiontermbynitelyintegratingcontroltorqueoverthetimeintervalrangingfromcurrenttimeminusthetimedelaytocurrenttime. 8 ].InNMEScontrol,theEMDismodeledasaninputdelay 106

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inthemusculoskeletaldynamics[ 6 ]andoccursduetoniteconductionvelocitiesofthechemicalionsinthemuscleinresponsetotheexternalelectricalinput[ 36 ].InputdelaycancauseperformancedegradationaswasobservedduringNMESexperimentaltrialsonhumansubjectswithRISEandNN+RISEcontrollersandhasalsobeenreportedtopotentiallycauseinstabilityduringhumanstanceexperimentswithNMES[ 40 ]. 3.4.4.1 ).ThedelayinNMES 107

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Controller PID PID+CPTR PD PD+CPTR TimeDelay Link1 Link2 Link1 Link2 Link1 Link2 Link1 Link2 0:106 0:107 0:129 0:089 1:954 3:137 7:629 0:164 0:172 0:204 0:149 3:430 6:484 14:960 RMSError ininverseinertiamatrix Link1 Link2 1:172 1:246 1:078 1:583 1:540 1:191 2:948 wasmeasuredasthedierencebetweenthetimewhenvoltageisappliedtothemuscleandthetimewhentheangleencoderdetectstherstlegmovement.Theinputdelayvaluesweremeasuredfortenhealthyindividuals(9maleand1female).Thetestsoneachindividualinvestigatedtheeectoninputdelayofthreestimulationparameters:frequency,pulsewidth,andvoltage.Threedierentsetoftestsincluding:frequencyvs 108

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Percentuncertainty PD+Compensator PID+Compensator ininputdelay Link1 Link2 Link1 Link2 1:159 1:234 1:079 1:338 1:192 1:451 1:452 1:629 1:186 3:528 1:229 4:099 3:260 4:331 3:182 inputdelay,voltagevsinputdelay,andpulsewidthvsinputdelaywereperformedoneachindividual.Ineachsetofexperiments,theothertwostimulationparameterswerekeptconstant.Beforethestartofexperiments,thesubjectwasinstructedtorelaxtoavoidvoluntarylegmotion.Thethresholdvoltagewasmeasuredforeachsubjectwhichcanbedenedastheminimumvoltageappliedtothesubject'smusclethatproducesamovementlargeenoughtobedetectedbytheangleencoder.Thismeasurementwasperformedbyapplyingaconstantinputvoltage,beginningat10Vandincreasingthevoltageslightlyuntilmovementwasdetected.Oncethethresholdvoltagewasobtained,theaforementionedthreesetsofexperimentswereperformedforeachindividual. Therstsetofexperimentsconstitutedvaryingfrequencywhilekeepingvoltageandpulsewidthconstant.Thesetestsconsistedofmeasuringtheinputdelayofthesubject'smuscleforthree0.2secondimpulses,each5secondsapart.Eachimpulseimpartedaconstantvoltage(thresholdvoltage+10V)tothemuscle.The5secondtimeseparationbetweentheimpulsesallowedthesubjectstovoluntarilybringtheirlegbacktothe 109

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restposition.Fig. 5-5 showsthetypicalEMDduringNMESinahealthyindividual.Finalinputdelayvaluewascomputedbyaveragingthemeasureddelayvaluesoverthreeimpulses.Eightexperimentswereperformedfordierentfrequencies,wherethefrequencywaschosenrandomlyfromtherangeof30Hzand100Hz(intrarangeintervalof10 110

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Hz).Thepulsewidthforthistypeoftheexperimentswaskeptat100s.Thesecondtypeofexperimentsconsistedofvaryingpulsewidthwhilekeepingvoltageandfrequencyconstant.Eachexperimentconstitutedthreeimpulsesasexplainedaboveforthefrequencytests.Nineexperimentswereperformedfordierentpulsewidths,wherepulsewidthwasrandomlychosenfrom100s.to1000s(intrarangeintervalof100s).Forthisset 111

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ofexperiments,thefrequencywaskeptconstantat30Hzandthevoltageconsistedofminimumthresholdvoltage+10V.Thelastsetofexperimentsinvolvedconductingexperimentswithvaryingvoltages.Sameimpulseprogramasusedintheearliersetofexperimentswasused,wherepulsewidthandfrequencywerekeptconstant.Thefrequencywaskeptat30Hzandthepulsewidthwaskeptat100mus.Threeexperimentswereperformedfordierentvoltages(thresholdvoltage+additionalvoltage,whereadditionalvoltagewasvariedbetween5and20volts(intrarangeintervalof5volts).Table 5-4 (asarepresentativeexample)showsthesummarizedinputdelayvariationswithrespecttodierentstimulationparametersinahealthyindividual. ANOVA(Analysisofvariance)testswereperformedtodeterminetheintraclasscorrelations.AnANOVAtestisgenerallyemployedtodeterminethestatisticalsignicancebetweenthemeansofdatagroupsnumberingmorethantwo(usingstudentt-testto 112

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5-6 )showedthatthedierenceinthemeansofEMDwasstatisticallysignicant(P-value=1:50372E10).Further,post-hoctestutilizingTukey'smethodshowedthattheEMDwaslongerforthelowerfrequenciesthanforthehigherfrequencies.Particularly,thetestshowedthattheaverageEMDof76msatafrequencyof30HzisstatisticallydierentfromtheaverageEMDof51msatafrequencyof100Hz.However,theresultsofthestimulationpulsewidth(seeFig. 5-8 )andvoltageexperiments(seeFig. 5-7 )showednosignicantcorrelationbetweeneithervaryingstimulationpulsewidthorstimulationvoltageandelectromechanicaldelay(P-value=0:6870and0:072,respectively). Figure5-6.Averageinputdelayvaluesacrossdierentfrequencies. 5{8 )and( 5{42 )istomeasureinertiaandinputdelayinthemuscledynamics.Implementingthecontrollerin( 5{8 )becomesevenmorecomplicatedduetothefactthatitrequiresnotonlyinertiaofthe 113

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musculoskeletal-LEMsystemtobemeasuredbutalsotheauxiliaryfunction(q;_q;t)2Rdenedin( 2{8 ),whichconsistsofunmeasurablemuscleforce-velocityandmuscleforce-lengthrelationshipstobeknown.However,thecontrollerdenedin( 5{42 )canbeimplementedprovidedthefollowingassumptionsaremade. 5-3 ). 3.3 )canbeupperboundedas wherea1;a2;a32Raresomeknownpositiveconstants,andBisthecontrolgainintroducedin( 5{37 ). 114

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ThetestbedforexperimentsconsistedofLEM(detailedinSection 3.4.4.1 ).Thecontrolobjectivewastotrackacontinuousconstantperiod(2sec.)sinusoidaltrajectory.Threehealthymales(age:21-28yrs)werechosenasthetestsubjects.Aftertheprotocol(seesection 3.4.4.1 ),theinputdelayvaluewasmeasuredforeachsubject.ThemeasureddelayvaluewasutilizedforimplementingthePDcontrollerwithdelaycompensationandthroughoutthedurationoftrials,thesamerespectivemeasureddelayvaluewasusedforeachsubject.TheexperimentscomparedthetraditionalPDcontrollerwiththePDcontrollerwithdelaycompensation.Eachsubjectparticipatedintwotofourtrialsforeachcontroller 5-5 .Thetableshowsbesttworesults(resultswithminimumRMSerrorsoutofalltrials)obtainedfromeachcontrollerandsubject. 115

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Pulsewidth[sec.] Voltage[V] 100 10 0.069 0.053 0.073 0.065 40 100 10 0.076 0.064 0.077 0.072 50 100 10 0.073 0.069 0.075 0.072 60 100 10 0.062 0.074 0.06 0.065 70 100 10 0.064 0.066 0.051 0.060 80 100 10 0.062 0.059 0.077 0.066 90 100 10 0.062 0.057 0.048 0.056 100 100 10 0.055 0.061 0.059 0.058 30 200 10 0.065 0.066 0.094 0.075 30 300 10 0.07 0.072 0.079 0.074 30 400 10 0.065 0.065 0.09 0.073 30 500 10 0.058 0.056 0.071 0.062 30 600 10 0.05 0.073 0.064 0.062 30 700 10 0.065 0.077 0.058 0.067 30 800 10 0.065 0.067 0.061 0.064 30 900 10 0.071 0.053 0.055 0.060 30 1000 10 0.057 0.083 0.065 0.068 30 100 5 0.081 0.061 0.061 0.068 30 100 15 0.068 0.079 0.087 0.078 30 100 20 0.082 0.084 0.059 0.075 Table5-4.Summarizedinputdelayvaluesofahealthyindividualacrossdierentstimulationparameters.Delayvalues()areshowninseconds.Thevoltagesshownincolumn3aretheaddedvoltagestothethresholdvoltage. AStudent'st-testwasalsoperformedtoconrmstatisticalsignicanceinthemeandierencesoftheRMSerrors,maximumsteadystateerrors(SSEs),RMSvoltages,andthemaximumvoltages.Thestatisticalcomparisonwasconductedontheaveragesofthetwobestresultsobtainedforeachsubject.TheanalysisshowsthatthemeandierencesintheRMSerrors,maximumSSEs,andmaximumvoltagesarestatisticallysignicantwhiletheanalysisshowsnostatisticaldierenceintheRMSvoltages.ThemeanRMSerrorof4:43obtainedwiththePDcontrollerwithdelaycompensationislowerthantheRMSerrorof6:03obtainedwiththePDcontroller.Also,themeanmaximumSSEandthemeanmaximumvoltageobtainedwiththePDcontrollerwithdelaycompensationarelowerthanthemeanmaximumSSEandthemeanmaximumvoltageobtainedwiththetraditionalPDcontroller.Therespectivep-valuesaregivenintheTable 5-5 .Theactual 116

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5-9 and 5-10 Figure5-9.Topplot:Actuallimbtrajectoryofasubject(solidline)versusthedesiredtrajectory(dashedline)inputobtainedwiththePDcontrollerwithdelaycompensation.Middleplot:Thetrackingerror(desiredangleminusactualangle)ofasubject'sleg,trackingaconstant(2sec.)perioddesiredtrajectory.Bottomplot:ThecomputedvoltageofthePDcontrollerwithdelaycompensationduringkneejointtracking. 117

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118

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RMSVoltage[V] Max.SSE Max.Voltage[V] Subject PD PD+CTR PD PD+CTR PD PD+CTR PD PD+CTR A 4:48 33:18 11:84 42:02 A 7:63 32:26 20:41 44:38 B 8:48 22:93 25:78 27:43 B 6:54 22:65 10:79 26:51 C 3:11 26:17 12:84 38:8 C 5:91 27:60 16:66 36:7 Mean 6:03 27:47 16:37 35:97 pvalue 0:003 0:008 wasrequiredtobeaknownconstant.Whilesomeapplicationshaveknowndelays(e.g.,teleoperation[ 129 ],somenetworkdelays[ 130 ],timeconstantsinbiologicalsystems[ 6 36 ]),thedevelopmentofmoregeneralizedresults(whichhavebeendevelopedforsomelinearsystems)withunknowntimedelaysremainsanopenchallenge.However,theexperimentalresultswithtwo-linkrobotillustratedsomerobustnesswithregardtotheuncertaintyinthetimedelay. 119

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96 ]basedRobustIntegraloftheSignoftheError(RISE)structure[ 11 27 ].Duetotheaddedbenetofreducedcontroleortandimprovedcontrolperformance,anadaptivecontrollerinconjunctionwithRISEfeedbackstructureisdesigned.However,sincethetimedelayvalueisnotalwaysknown,itbecomeschallengingtodesignadelayfreeadaptivecontrollaw.Throughtheuseofadesiredcompensationadaptivelaw(DCAL)basedtechniqueandsegregatingtheappropriatetermsintheopenlooperrorsystem,thedependenceofparameterestimatelawsonthetimedelayedunknownregressionmatrixisremoved.Contrarytopreviousresults,thereisnosingularityinthedevelopedcontroller.ALyapunov-basedstabilityanalysisisprovidedthatusesanLKfunctionalalongwithYoung'sinequalitytoremovetimedelayedtermsandachievesasymptotictracking. 87 ] _x1=x2_xn1(t)=xn_xn(t)=f(x(t))+1(x(t))+g(x(t))+2(x(t))+d(t)+bu(t)y=x1 120

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6{1 ),f(x(t));1(x(t))2Rmareunknownfunctions,g(x(t));2(x(t))2Rmareunknowntime-delayedfunctions,2R+isanunknownconstantarbitrarilylargetimedelay,d(t)2Rmisaboundeddisturbance,b2Risanunknownpositiveconstant,u(t)2Rmisthecontrolinput,andx(t)=xT1xT2:::xTnT2Rmndenotesystemstates,wherex(t)isassumedtobemeasurable.Alsothefollowingassumptionsandnotationswillbeexploitedinthesubsequentdevelopment. 83 87 95 ]). 121

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where1;:::;n2Rdenotepositiveconstantcontrolgains.Asdenedin( 6{5 ),thelteredtrackingerrorr(t)isnotmeasurablesincetheexpressiondependson_xn(t):However,e1(t);:::;en(t)2Rmaremeasurablebecause( 6{4 )canbeexpressedintermsofthetrackingerrore1(t)as whereaij2Rarepositiveconstantsobtainedfromsubstituting( 6{6 )in( 6{4 )andcomparingcoecients[ 114 ].Itcanbeeasilyshownthat Using( 6{2 )-( 6{7 ),theopenlooperrorsystemcanbewrittenas wherel(e1;_e1;:::;e(n1)1)2Rmisafunctionofknownandmeasurableterms,denedasl=n2Xj=0anje(j+1)1+ne(j)1+ne(n1)1: 6{8 )byb1andutilizingtheexpressionsin( 6{1 )andAssumption1toobtainthefollowingexpression: 122

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6{9 )canberewrittenas wherexd=hyTd_yTd:::y(n1)TdiT2Rmndenotesacolumnvectorcontainingthedesiredtrajectoryanditsderivatives.Thegroupingoftermsandstructureof( 6{10 )ismotivatedbythesubsequentstabilityanalysisandtheneedtodevelopanadaptiveupdatelawthatisinvarianttotheunknowntimedelay.TheauxiliaryfunctionS1(xd;x)isdenedbecausethesetermsarenotfunctionsofthetime-delay.TheauxiliaryfunctionS2(xd;x)isintroducedbecausethetime-delayedstatesareisolatedinthisterm,andW(xd;xd;_xd)isisolatedbecauseitonlycontainsfunctionsofthedesiredtrajectory. Basedontheopen-looperrorsystemin( 6{10 ),thecontrolinputu(t)2Rmisdesignedas In( 6{13 ),2Rmdenotestheimplicitlearning-based[ 96 ]RISEtermdenedasthegeneralizedsolutionto _=(ks+1)r+sgn(en);(0)=0;(6{14) 123

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6{13 ),^1(t)2Rp1;^2(t)2Rp2denoteparameterestimatevectorsdenedas _^1=1_YT1(xd)r;_^2=2_YT2(xd)r;(6{15a) where12Rp1p1,22Rp2p2areknown,constant,diagonal,positivedeniteadaptationgainmatrices.In( 6{15a );_YT2(xd)doesnotdependonthetimedelayeddesiredstate.ThisdelayfreelawisachievedbyisolatingthedelayedtermY2(xd)2intheauxiliarysignalW(xd;xd;y(n)d)in( 6{12 ).Theadaptationlawsin( 6{15a )dependontheunmeasurablesignalr(t);butbyusingthefactthat_Y1(xd),_Y2(xd)arefunctionsoftheknowntimevaryingdesiredtrajectory,integrationbypartscanbeusedtoimplement^i(t)fori=1;2whereonlyen(t)isrequiredas ^i=^i(0)+i_YTi(xd)en()jt0it0nYTi(xd)en()n_YTi(xd)en()od: 6{13 )into( 6{10 )as where~ifori=1;2aretheparameterestimationerrorvectorsdenedas ~i=i^i:(6{17) TofacilitatethesubsequentstabilityanalysisandtomoreclearlyillustratehowtheRISEstructurein( 6{14 )isusedtorejectthedisturbanceterms,thetimederivativeof( 6{16 )isdeterminedas 124

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~N=b1_l+_S1+_S2+enY1(xd)1_Y1(xd)rY2(xd)2_Y2(xd)r;Nd=_W+b1_d: UsingAssumptions2,3;and4,Nd(xd;_xd;xd;t)anditstimederivativecanbeupperboundedas whereNd;_Nd2Rareknownpositiveconstants.Theexpressiondenedin( 6{19 )canbeupperboundedusingtheMeanValueTheoremas[ 114 ] wherez(t)2R(n+1)misdenedas andtheknownboundingfunctions1(kzk);2(kzk)2Rarepositive,globallyinvertible,andnondecreasingfunctions.Notethattheupperboundfortheauxiliaryfunction~N(e1;e2;e1;e2)in( 6{21 )issegregatedintodelayfreeanddelayedupperboundfunctions.MotivationforthissegregationoftermsistoeliminatethedelaydependenttermthroughtheuseofanLKfunctionalinthestabilityanalysis.Specically,letQ(t)2RdenoteanLKfunctionaldenedas 2ksZtt22(kz()k)kz()k2d;(6{23) whereks2Rand2()areintroducedin( 6{14 )and( 6{21 ),respectively. Theorem6. 6{13 ),( 6{14 ),and( 6{15a )ensuresthatallsystemsignalsareboundedunderclosed-loopoperation.Thetrackingerrorisregulatedinthe

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6{14 )isselectedsucientlylarge,andn1;n;andareselectedaccordingtothefollowingsucientconditions: 2(6{24) 6{4 )and( 6{5 ),respectively;isintroducedin( 6{14 );andNdand_Ndareintroducedin( 6{20 ). where~i(t)aredenedin( 6{17 ),z(t)andQ(t)aredenedin( 6{22 )and( 6{23 ),respectively,andtheauxiliaryfunctionP(t)2Risthegeneralizedsolutiontothedierentialequation _P(t)=L(t);P(0)=nXi=1jeni(0)jen(0)TNd(0)(6{26) TheauxiliaryfunctionL(t)2Rin( 6{26 )isdenedas ProvidedthesucientconditionsstatedinTheorem 6 aresatised,thenP(t)0(seetheAppendixB). LetVL(y;t):D[0;1)!RdenoteaLipschitzcontinuousregularpositivedenitefunctionaldenedas 2eT1e1+1 2eT2e2+:::+1 2eTnen+1 2rTb1r+P+Q+1 2~T111~1 +1 2~T212~2;

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providedthesucientconditionsintroducedinTheorem 6 aresatised.In( 6{29 ),U1(y);U2(y)2Rarecontinuous,positivedenitefunctionsdenedas where1;22Raredenedas 2min(1;b1;min11;min12);2=max(1 2b1;1;max11;max12); andminfg;maxfgdenotetheminimumandmaximumEigenvalues,respectively.Aftertakingthetimederivativeof( 6{28 ),_VL(y;t)canbeexpressedas_VL(y;t),eT1_e1+eT2_e2+:::+eTn_en+rTb1_r+_P+_Q+~T111~1+~T212~2: 6{3 ),( 6{4 ),( 6{18 ),( 6{26 ),( 6{27 ),adaptationlawsin( 6{15a ),andthetimederivativeofQ(t)in( 6{23 ),someofthedierentialequationsdescribingtheclosed-loopsystemforwhichthestabilityanalysisisbeingperformedhavediscontinuousright-hand 127

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_e1=e21e1; _e2=e32e2e1; _en=rnen _P(t)=rT(Nd(t)sgn(en)); _Q(t)=1 2ks22(kz(t)k)kz(t)k222(kz(t)k)kz(t)k2; ~T111~1=~T1_YT1(xd)r; ~T212~2=~T2_YT2(xd)r: Letf(y;t)2R(n+1)m+p1+p2+2denotetherighthandsideof( 6{32 ).f(y;t)iscontinuousexceptinthesetf(y;t)je2=0g.From[ 103 { 106 ],anabsolutecontinuousFilippovsolutiony(t)existsalmosteverywhere(a.e.)sothat_y2K[f](y;t)a:e: 6{28 )existsa.e.,and_VL(y;t)2a:e:~VL(y;t)where 2P1 2_P1 2Q1 2_Q~1~2T; =rVTLK_e1_e2_en_r1 2P1 2_P1 2Q1 2_Q~1~2T;eT1eT2eTnrTb12P1 22Q1 2~T111~T212K_e1_e2_en_r1 2P1 2_P1 2Q1 2_Q~1~2T:

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6{32 to 6{33 anddiscussion,seeSection 3.3.1 .Afterutilizing( 6{3 ),( 6{4 ),( 6{18 ),( 6{26 ),( 6{27 ),adaptationlawsin( 6{15a )andthetimederivativeofQ(t)in( 6{23 ),theexpressionin( 6{33 )canberewrittenas 2ks22(kzk)kzk222(kzk)kzk2: Cancellingcommontermsyieldsandusing( 6{21 ) AfterapplyingfollowingYoung'sinequalitytodeterminethat 2ken1k2+kenk2;(6{36) theexpressionin( 6{35 )canbewrittenas~VL(y;t)n2Xi=1ikeik2n11 2ken1k2n1 2kenk2krk2ks 6{37 )canbewrittenas 2kskzk2(6{37) where2(kzk)2Risdenedas and34=min1;2;:::;n2;n11 2;n1 2;1:Theboundingfunction(kzk)isapositive,globallyinvertible,andnondecreasingfunctionthatdoesnotdependon 129

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6{37 )canbefurtherupperboundedbyacontinuous,positivesemi-denitefunction forsomepositiveconstantc,whereD,ny(t)2R(n+1)m+p1+p2+2jkyk1p 6{29 )and( 6{39 )canbeusedtoshowthatV(y;t)2L1inD;hence,e1;e2;:::;en;~1;~22L1inD.Theclosed-looperrorsystemscannowbeusedtoconcludeallremainingsignalsareboundedinD,andthedenitionsforU(y)andz(t)canbeusedtoprovethatU(y)isuniformlycontinuousinD.LetSDdenoteasetdenedas Theregionofattractionin( 6{40 )canbemadearbitrarilylargetoincludeanyinitialconditionsbyincreasingthecontrolgainks(i.e.,asemi-globalstabilityresult),andhence Basedonthedenitionofz(t),( 6{41 )canbeusedtoshowthat 87 ]: _x1=x2 _x2=f(x)+g(x)+1(x)+2(x)+d+bu;

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Thefollowinggainsarechosenfor=3sand=10sks=10;1=7;2=6;=5;1=0:5;2=[2;0;0;10]. FromtheresultsshowninFigs. 6-1 6-5 ,itisclearthatthecontrollertracksthetimevaryingdesiredtrajectoryeectively.Inboththecases,thesteadystateerrorsstaybetween0:003radiansandthecontrolinputsarebounded:Alsoitcanbeseenthatthereisalittlevariationinthecontrolperformancesfortimedelays=3sand=10s: 131

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132

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Figure6-4.Trackingerrorforthecase=10s:

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134

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3 dealwithunknownnonlinearmapping,boundeddisturbances,andotherunknownnonlinearitiesanduncertainties.TheLyapunov-basedstabilityanalysisisutilizedtoprovesemi-globalasymptoticstabilityforthecontrollers.ExtensiveexperimentsonhealthyvolunteerswereconductedforbothRISEandNN+RISEcontrollers.Particularly,itwasshownthattheinclusionofneuralnetworkbasedfeedforwardcomponentintheRISEcontrollerimprovesperformanceduringNMES.Also,preliminaryexperimentaltrialsdemonstratingsit-to-standtaskdepictedthefeasibilityoftheNN+RISEcontrollerinaclinical-typescenario. InChapter 4 ,aNN-basedcontrollerisdevelopedtocompensateforfatigue.Thebenetofthecontrolleristhatitincorporatesmoremuscledynamicsknowledgenamely,calciumandfatiguedynamics.Theeectivenessofthecontrollertocompensatefatigueisshownthroughsimulationresults.FurthersimulationsshowthatthecontrollerperformsbetterthantheRISEcontroller. AnimportanttechnicaldicultyinNMESisinputdelaywhichbecomesmorechallengingduetothepresenceofunknownnonlinearitiesanddisturbances.Lackofinputdelaycompensatingcontrollersforuncertainnonlinearsystemsmotivatedtodeveloppredictor-basedcontrollersforgeneralEulerLagrangesysteminChapter 5 .TheLyapunov-basedstabilityanalysisutilizesLKfunctionalstoprovesemi-globalUUBtracking.ExtensiveexperimentalresultsshowbetterperformanceofthecontrollerincomparisontothetraditionalPD/PIDcontrolleraswellastheirrobustnesstouncertainty 135

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ThelastchapterinthedissertationcoversthedevelopmentofRISE-basedadaptivecontrollerforaclassofnonlinearsystemwithstatedelays.Thesignicanceoftheresultisthatarobustandcontinuouscontrollerisdevelopedforanonlinearsystemwithunknownstatedelaysandadditivedisturbances.Lyapunov-basedstabilityanalysisaidedwithLKfunctionalsisutilizedtoshowasemi-globalasymptotictracking. 3 showedthattheRMSerrordierence(forbothRISEandNN+RISEcontrollers)betweentheexionandextensionphaseofthelegmovementisstatisticallysignicant.Theseresultssuggestthattheroleofswitchingcontrollers(hybridcontrolapproach)canbeinvestigated.Specically,twodierentcontrollerscanbeutilizedwhereeachcontrollerisdedicatedforaparticularphaseofthelegmovement. 4 hasthreemainlimitations:unmeasurablecalciumandfatiguedynamics,dependenceonacceleration,anduniformlyultimatelyboundedstabilityresult.Eortscanbemadetodevelopanobserver-basedcontrollertoremovethedependenceonmathematicalfatigueandcalciumdynamicsmodels.Specically,recurrentneuralnetworkbasedobservercanbedesignedtoidentifysystemstates.Further,improvementinstabilityanalysiscanbeachievedbydevelopingacontrollerwithasymptotictracking.AnextensiveinvestigationisrequiredtoobservetheeectofthecontrollerinChapter 4 onreducingfatigue.Experimentsshouldnotonlycomparetheresultwithanexistingcontrollerforimprovedperformancebutshouldspecicallystudytheeectivenessoftheincludedfatiguemodelforfatiguecompensation.Theresultsmay(ormaynot) 136

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131 ],whichcanbesurgicallyimplantedatspecicsitesinthemuscle.Thesemicroelectrodeswhichdonotrequirewiresarepoweredexternallythroughaninductivecoilandabattery.MultipleBIONstostimulatespecicmusclesitescannotonlybeusedtoproducedesiredfunctionalmovementsbutalsocanbeusedtoeliminatemusclefatiguethroughutilizingnon-repetitiveandselectivemusclerecruitment.InordertoproduceNMEScontrolviaBIONs,studieswillberequired 137

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5 showedthattheinputdelayinNMESdependsonlyonvaryingfrequency.However,furtherinvestigationsarerequiredtostudytheeectoffatigueandnon-isometriccontractionsoninputdelay.Also,resultsinChapter 5 areonlyapplicablewithknownconstantinputdelayvalues.Therefore,controllersneedtobedevelopedtoaccountfortime-varyingorunknowninputdelay.Otherdelaycompensatingtechniquessuchasmodelpredictivecontrol(MPC)canalsobeinvestigatedforNMES.OneoftheadvantagesofMPCisthatitinherentlycompensatesforinputdelays.Althoughthetechniquewouldrequiremuscledynamicstobeknown,advantagessuchasperformanceandcontroloptimizationinadditiontodelaycompensationmakesMPCaworthycandidateforinvestigation. 138

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_Q(t)=!d dtZttZtsk_u()k2dds; _Q(t)=!Zttk_u()k2ddt dt!Zttk_u()k2dd(t) @tZtsk_u()k2dds:(1{3) Theexpressionin( 1{3 )canbesimpliedas @tZtsk_u()k2dds:(1{4) AgainapplyingLeibnizintegralruleonsecondintegralin( 1{4 ) dtk_u(s)k2ds dt+Zts@ @tk_u()k2dds:(1{5) Theexpressionin( 1{5 )canbesimpliedas Furtherintegratingthesecondintegralin( 1{6 )

PAGE 140

1 140

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Proof:Integratingbothsidesof( 2{9 ) Onsubstituting( 6{5 )in( 2{12 )yieldsZt0L()d=Zt0_eTnNddZt0_eTnsgn(en)d+Zt0eTn(Ndsgn(en))d: Afterutilizingintegrationbypartsfortherstintegralandintegratingthesecondintegralin( 2{13 ),thefollowingexpressionisobtained:Zt0L()d=eTnNdeTn(0)Nd(0)+nXi=1jeni(0)jnXi=1jeni(t)j+Zt0eTn(Nd1 wherethefactthatsgn(en)canbedenotedas 141

PAGE 142

6{20 )andthefactthat theexpressionin( 2{14 )canbeupperboundedasZt0L()dnXi=1jeni(0)jeTn(0)Nd(0)+(Ndkenk)+Zt0kenkNd+_Nd Itisclearfrom( 2{17 )thatifthefollowingsucientcondition issatised,thenthefollowinginequalityholds 142

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[1] P.H.PeckhamandD.B.Gray,\Functionalneuromuscularstimulation,"J.Rehabil.Res.Dev.,vol.33,pp.9{11,1996. [2] P.H.PeckhamandJ.S.Knutson,\Functionalelectricalstimulationforneuromuscularapplications,"Annu.Rev.Biomed.Eng.,vol.7,pp.327{360,2005. [3] J.J.AbbasandH.J.Chizeck,\Feedbackcontrolofcoronalplanehipangleinparaplegicsubjectsusingfunctionalneuromuscularstimulation,"IEEETrans.Biomed.Eng.,vol.38,no.7,pp.687{698,1991. [4] N.Lan,P.E.Crago,andH.J.Chizeck,\Controlofend-pointforcesofamultijointlimbbyfunctionalneuromuscularstimulation,"IEEETrans.Biomed.Eng.,vol.38,no.10,pp.953{965,1991. [5] ||,\Feedbackcontrolmethodsfortaskregulationbyelectricalstimulationofmuscles,"IEEETrans.Biomed.Eng.,vol.38,no.12,pp.1213{1223,1991. [6] T.Schauer,N.O.Negard,F.Previdi,K.J.Hunt,M.H.Fraser,E.Ferchland,andJ.Raisch,\Onlineidenticationandnonlinearcontroloftheelectricallystimulatedquadricepsmuscle,"ControlEng.Pract.,vol.13,pp.1207{1219,2005. [7] K.Stegath,N.Sharma,C.M.Gregory,andW.E.Dixon,\Anextremumseekingmethodfornon-isometricneuromuscularelectricalstimulation,"inProc.IEEEInt.Conf.Syst.Man.Cybern.,2007,pp.2528{2532. [8] A.H.Vette,K.Masani,andM.R.Popovic,\ImplementationofaphysiologicallyidentiedPDfeedbackcontrollerforregulatingtheactiveankletorqueduringquietstance,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.15,no.2,pp.235{243,June2007. [9] G.KhangandF.E.Zajac,\Paraplegicstandingcontrolledbyfunctionalneuromuscularstimulation:PartI-computermodelandcontrol-systemdesign,"IEEETrans.Biomed.Eng.,vol.36,no.9,pp.873{884,1989. [10] F.Previdi,M.Ferrarin,S.Savaresi,andS.Bittanti,\Gainschedulingcontroloffunctionalelectricalstimulationforassistedstandingupandsittingdowninparaplegia:asimulationstudy,"Int.J.AdaptControlSignalProcess.,vol.19,pp.327{338,2005. [11] P.M.Patre,W.MacKunis,C.Makkar,andW.E.Dixon,\Asymptotictrackingforsystemswithstructuredandunstructureduncertainties,"IEEETrans.ControlSyst.Technol.,vol.16,no.2,pp.373{379,2008. [12] ||,\Asymptotictrackingforsystemswithstructuredandunstructureduncertainties,"inProc.IEEEConf.Decis.Control,SanDiego,CA,Dec.2006,pp.441{446. 143

PAGE 144

N.Lan,H.Feng,andE.Crago,\Neuralnetworkgenerationofmusclestimulationpatternsforcontrolofarmmovements,"IEEETrans.Rehabil.Eng.,vol.2,no.4,pp.213{224,1994. [14] J.J.AbbasandH.J.Chizeck,\Neuralnetworkcontroloffunctionalneuromuscularstimulationsystems:computersimulationstudies,"IEEETrans.Biomed.Eng.,vol.42,no.11,pp.1117{1127,Nov.1995. [15] D.GraupeandH.Kordylewski,\ArticialneuralnetworkcontrolofFESinparaplegicsforpatientresponsiveambulation,"IEEETrans.Biomed.Eng.,vol.42,no.7,pp.699{707,July1995. [16] G.-C.Chang,J.-J.Lub,G.-D.Liao,J.-S.Lai,C.-K.Cheng,B.-L.Kuo,andT.-S.Kuo,\Aneuro-controlsystemforthekneejointpositioncontrolwithquadricepsstimulation,"IEEETrans.Rehabil.Eng.,vol.5,no.1,pp.2{11,Mar.1997. [17] J.A.RiessandJ.J.Abbas,\Adaptiveneuralnetworkcontrolofcyclicmovementsusingfunctionalneuromuscularstimulation,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.8,pp.42{52,2000. [18] H.KordylewskiandD.Graupe,\Controlofneuromuscularstimulationforambulationbycompleteparaplegicsviaarticialneuralnetworks,"Neurol.Res.,vol.23,no.5,pp.472{481,2001. [19] D.G.ZhangandK.Y.Zhu,\SimulationstudyofFES-assistedstandingupwithneuralnetworkcontrol,"inProc.Annu.Int.Conf.IEEEEng.Med.Biol.Soc.,vol.6,2004,pp.4118{4121. [20] J.P.GiuridaandP.E.Crago,\Functionalrestorationofelbowextensionafterspinal-cordinjuryusinganeuralnetwork-basedsynergisticFEScontroller,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.13,no.2,pp.147{152,2005. [21] Y.-L.Chen,W.-L.Chen,C.-C.Hsiao,T.-S.Kuo,andJ.-S.Lai,\DevelopmentoftheFESsystemwithneuralnetwork+PIDcontrollerforthestroke,"inProc.IEEEInt.Symp.CircuitsSyst.,May23{26,2005,pp.5119{5121. [22] K.Kurosawa,R.Futami,T.Watanabe,andN.Hoshimiya,\JointanglecontrolbyFESusingafeedbackerrorlearningcontroller,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.13,pp.359{371,2005. [23] A.Pedrocchi,S.Ferrante,E.DeMomi,andG.Ferrigno,\Errormappingcontroller:aclosedloopneuroprosthesiscontrolledbyarticialneuralnetworks,"J.Neuroeng.Rehabil.,vol.3,no.1,p.25,2006. [24] S.Kim,M.Fairchild,A.Iarkov,J.Abbas,andR.Jung,\Adaptivecontrolforneuromuscularstimulation-assistedmovementtherapyinarodentmodel,"IEEETrans.Biomed.Eng.,vol.56,pp.452{461,2008. 144

PAGE 145

A.AjoudaniandA.Erfanian,\Aneuro-sliding-modecontrolwithadaptivemodelingofuncertaintyforcontrolofmovementinparalyzedlimbsusingfunctionalelectricalstimulation,"IEEETrans.Biomed.Eng.,vol.56,no.7,pp.1771{1780,Jul.2009. [26] J.LujanandP.Crago,\Automatedoptimalcoordinationofmultiple-DOFneuromuscularactionsinfeedforwardneuroprostheses,"IEEETrans.Biomed.Eng.,vol.56,no.1,pp.179{187,Jan.2009. [27] P.M.Patre,W.MacKunis,K.Kaiser,andW.E.Dixon,\AsymptotictrackingforuncertaindynamicsystemsviaamultilayerneuralnetworkfeedforwardandRISEfeedbackcontrolstructure,"IEEETrans.Autom.Control,vol.53,no.9,pp.2180{2185,2008. [28] M.J.Levy,M.andZ.Susak,\Recruitment,forceandfatiguecharacteristicsofquadricepsmusclesofparaplegics,isometricallyactivatedbysurfaceFES,"J.Biomed.Eng.,vol.12,pp.150{156,1990. [29] D.Russ,K.Vandenborne,andS.Binder-Macleod,\Factorsinfatigueduringintermittentelectricalstimulationofhumanskeletalmuscle,"J.Appl.Physiol.,vol.93,no.2,pp.469{478,2002. [30] J.Mizmhi,\Fatigueinmusclesactivatedbyfunctionalelectricalstimulation,"Crit.Rev.Phys.Rehabil.Med.,vol.9,no.2,pp.93{129,1997. [31] E.Asmussen,\Musclefatigue,"Med.Sci.Sports.Exerc.,vol.11,no.4,pp.313{321,1979. [32] R.Maladen,R.Perumal,A.Wexler,andS.Binder-Macleod,\Eectsofactivationpatternonnonisometrichumanskeletalmuscleperformance,"J.Appl.Physiol.,vol.102,no.5,pp.1985{91,2007. [33] S.Binder-Macleod,J.Dean,andJ.Ding,\Electricalstimulationfactorsinpotentiationofhumanquadricepsfemoris,"MuscleNerve,vol.25,no.2,pp.271{9,2002. [34] Y.Giat,J.Mizrahi,andM.Levy,\AmusculotendonmodelofthefatigueprolesofparalyzedquadricepsmuscleunderFES,"IEEETrans.Biomed.Eng.,vol.40,no.7,pp.664{674,1993. [35] R.Riener,J.Quintern,andG.Schmidt,\Biomechanicalmodelofthehumankneeevaluatedbyneuromuscularstimulation,"J.Biomech.,vol.29,pp.1157{1167,1996. [36] R.RienerandT.Fuhr,\Patient-drivencontrolofFES-supportedstandingup:Asimulationstudy,"IEEETrans.Rehabil.Eng.,vol.6,pp.113{124,1998. [37] J.Ding,A.Wexler,andS.Binder-Macleod,\Apredictivefatiguemodel.I.predictingtheeectofstimulationfrequencyandpatternonfatigue,"IEEETrans.Rehabil.Eng.,vol.10,no.1,pp.48{58,2002. 145

PAGE 146

||,\Apredictivefatiguemodel.II.predictingtheeectofrestingtimesonfatigue,"IEEETrans.Rehabil.Eng.,vol.10,no.1,pp.59{67,2002. [39] S.Jezernik,R.Wassink,andT.Keller,\Slidingmodeclosed-loopcontrolofFES:Controllingtheshankmovement,"IEEETrans.Biomed.Eng.,vol.51,pp.263{272,2004. [40] K.Masani,A.Vette,N.Kawashima,andM.Popovic,\Neuromusculoskeletaltorque-generationprocesshasalargedestabilizingeectonthecontrolmechanismofquietstanding,"J.Neurophysiol.,vol.100,no.3,p.1465,2008. [41] S.Evesque,A.Annaswamy,S.Niculescu,andA.Dowling,\Adaptivecontrolofaclassoftimedelaysystems,"J.Dyn.Syst.Meas.Contr.,vol.125(2),pp.186{193,2003. [42] J.HuangandF.Lewis,\Neural-networkpredictivecontrolfornonlineardynamicsystemswithtime-delay,"IEEETrans.NeuralNetworks,vol.14,no.2,pp.377{389,2003. [43] D.YanakievandI.Kanellakopoulos,\LongitudinalcontrolofautomatedCHVswithsignicantactuatordelays,"IEEETrans.Veh.Technol.,vol.50,no.5,pp.1289{1297,2001. [44] B.Bequette,\Nonlinearcontrolofchemicalprocesses:Areview,"Ind.Eng.Chem.Res.,vol.30,no.7,pp.1391{1413,1991. [45] J.-P.Richard,\Time-delaysystems:anoverviewofsomerecentadvancesandopenproblems,"Automatica,vol.39,no.10,pp.1667{1694,2003. [46] K.Gu,V.L.Kharitonov,andJ.Chen,StabilityofTime-delaysystems.Birkhauser,2003. [47] W.KwonandA.Pearson,\Feedbackstabilizationoflinearsystemswithdelayedcontrol,"IEEETrans.Autom.Control,vol.25,no.2,pp.266{269,1980. [48] Z.Artstein,\Linearsystemswithdelayedcontrols:Areduction,"IEEETrans.Autom.Control,vol.27,no.4,pp.869{879,1982. [49] Y.FiagbedziandA.Pearson,\Feedbackstabilizationoflinearautonomoustimelagsystems,"IEEETrans.Autom.Control,vol.31,no.9,pp.847{855,1986. [50] M.Jankovic,\Recursivepredictordesignforlinearsystemswithtimedelay,"inProc.IEEEAm.ControlConf.,June2008,pp.4904{4909. [51] A.ManitiusandA.Olbrot,\Finitespectrumassignmentproblemforsystemswithdelays,"IEEETrans.Autom.Control,vol.24,no.4,pp.541{552,1979. 146

PAGE 147

S.MondieandW.Michiels,\Finitespectrumassignmentofunstabletime-delaysystemswithasafeimplementation,"IEEETrans.Autom.Control,vol.48,no.12,pp.2207{2212,2003. [53] Y.RohandJ.Oh,\Robuststabilizationofuncertaininput-delaysystemsbyslidingmodecontrolwithdelaycompensation,"Automatica,vol.35,pp.1861{1865,1999. [54] M.Krstic,\Lyapunovtoolsforpredictorfeedbacksfordelaysystems:Inverseoptimalityandrobustnesstodelaymismatch,"Automatica,vol.44,no.11,pp.2930{2935,2008. [55] M.KrsticandD.Bresch-Pietri,\Delay-adaptivefull-statepredictorfeedbackforsystemswithunknownlongactuatordelay,"inProc.IEEEAm.ControlConf.,2009,pp.4500{4505. [56] D.Bresch-PietriandM.Krstic,\Adaptivetrajectorytrackingdespiteunknowninputdelayandplantparameters,"Automatica,vol.45,no.9,pp.2074{2081,2009. [57] M.KrsticandA.Smyshlyaev,\Backsteppingboundarycontrolforrst-orderhyperbolicPDEsandapplicationtosystemswithactuatorandsensordelays,"Syst.Contr.Lett.,vol.57,no.9,pp.750{758,2008. [58] W.Michiels,K.Engelborghs,P.Vansevenant,andD.Roose,\Continuouspoleplacementfordelayequations,"Automatica,vol.38,no.5,pp.747{761,2002. [59] O.M.Smith,\Acontrollertoovercomedeadtime,"ISAJ.,vol.6,pp.28{33,1959. [60] M.MatausekandA.Micic,\Amodiedsmithpredictorforcontrollingaprocesswithanintegratorandlongdead-time,"IEEETrans.Autom.Control,vol.41,no.8,pp.1199{1203,Aug1996. [61] S.MajhiandD.Atherton,\Modiedsmithpredictorandcontrollerforprocesseswithtimedelay,"IEEProc.Contr.Theor.Appl.,vol.146,no.5,pp.359{366,1999. [62] W.ZhangandY.Sun,\Modiedsmithpredictorforcontrollingintegrator/timedelayprocesses,"Ind.Eng.Chem.Res.,vol.35,no.8,pp.2769{2772,1996. [63] A.NortclieandJ.Love,\Varyingtimedelaysmithpredictorprocesscontroller,"ISATrans.,vol.43,no.1,pp.61{71,2004. [64] P.GarciaandP.Albertos,\Anewdead-timecompensatortocontrolstableandintegratingprocesseswithlongdead-time,"Automatica,vol.44,no.4,pp.1062{1071,2008. [65] S.MajhiandD.Atherton,\Obtainingcontrollerparametersforanewsmithpredictorusingautotuning,"Automatica,vol.36,no.11,pp.1651{1658,Nov2000. [66] I.Chien,S.Peng,andJ.Liu,\Simplecontrolmethodforintegratingprocesseswithlongdeadtime,"J.ProcessControl,vol.12,no.3,pp.391{404,2002. 147

PAGE 148

L.Roca,J.LuisGuzman,J.E.Normey-Rico,M.Berenguel,andL.Yebra,\Robustconstrainedpredictivefeedbacklinearizationcontrollerinasolardesalinationplantcollectoreld,"ControlEng.Pract.,vol.17,no.9,pp.1076{1088,Sep.2009. [68] C.Xiang,L.Cao,Q.Wang,andT.Lee,\Designofpredictor-basedcontrollersforinput-delaysystems,"inProc.IEEEInt.Symp.Ind.Electron.,302008-July22008,pp.1009{1014. [69] H.-H.Wang,\Optimalvibrationcontrolforoshorestructuressubjectedtowaveloadingwithinputdelay,"inInt.Conf.Meas.Technol.Mechatron.Autom.,vol.2,April2009,pp.853{856. [70] ||,\Optimaltrackingfordiscrete-timesystemswithinputdelays,"inProc.Chin.ControlDecis.Conf.,July2008,pp.4033{4037. [71] H.-H.Wang,N.-P.Hu,andB.-L.Zhang,\Anoptimalcontrolregulatorfornonlineardiscrete-timesystemswithinputdelays,"inWorldCongr.Intell.ControlAutom.,June2008,pp.5540{5544. [72] M.KrsticandA.Smyshlyaev,BoundarycontrolofPDEs:AcourseonBacksteppingDesigns.SIAM,2008. [73] S.NiculescuandA.Annaswamy,\Anadaptivesmith-controllerfortime-delaysystemswithrelativedegreen*<2,"Syst.Contr.Lett.,vol.49,no.5,pp.347{358,2003. [74] C.KravarisandR.Wright,\Deadtimecompensationfornonlinearprocesses,"AIChEJ.,vol.35,no.9,pp.1535{1542,1989. [75] M.HensonandD.Seborg,\Timedelaycompensationfornonlinearprocesses,"Ind.Eng.Chem.Res.,vol.33,no.6,pp.1493{1500,1994. [76] F.MazencandP.Bliman,\Backsteppingdesignfortime-delaynonlinearsystems,"IEEETrans.Autom.Control,vol.51,no.1,pp.149{154,2006. [77] M.Jankovic,\Controlofcascadesystemswithtimedelay-theintegralcross-termapproach,"inProc.IEEEConf.Decis.Control,Dec.2006,pp.2547{2552. [78] A.Teel,\ConnectionsbetweenRazumikhin-typetheoremsandtheISSnonlinearsmallgaintheorem,"IEEETrans.Autom.Control,vol.43,no.7,pp.960{964,1998. [79] M.Krstic,\Oncompensatinglongactuatordelaysinnonlinearcontrol,"IEEETrans.Autom.Control,vol.53,no.7,pp.1684{1688,2008. [80] Y.XiaandY.Jia,\Robustsliding-modecontrolforuncertaintime-delaysystems:anLMIapproach,"IEEETrans.Autom.Control,vol.48,no.6,pp.1086{1091,June2003. 148

PAGE 149

X.-J.Jing,D.-L.Tan,andY.-C.Wang,\AnLMIapproachtostabilityofsystemswithseveretime-delay,"IEEETrans.Autom.Control,vol.49,no.7,pp.1192{1195,July2004. [82] S.Nguang,\Robuststabilizationofaclassoftime-delaynonlinearsystems,"IEEETrans.Autom.Control,vol.45,no.4,pp.756{762,2000. [83] S.Ge,F.Hong,andT.H.Lee,\Adaptiveneuralnetworkcontrolofnonlinearsystemswithunknowntimedelays,"IEEETrans.Autom.Control,vol.48,no.11,pp.2004{2010,Nov.2003. [84] S.MondieandV.Kharitonov,\Exponentialestimatesforretardedtime-delaysystems:anLMIapproach,"IEEETrans.Autom.Control,vol.50,no.2,pp.268{273,Feb.2005. [85] D.Ho,L.Junmin,andY.Niu,\Adaptiveneuralcontrolforaclassofnonlinearlyparametrictime-delaysystems,"IEEETrans.NeuralNetworks,vol.16,pp.625{635,2005. [86] X.LiandC.deSouza,\Delay-dependentrobuststabilityandstabilizationofuncertainlineardelaysystems:alinearmatrixinequalityapproach,"IEEETrans.Autom.Control,vol.42,no.8,pp.1144{1148,Aug1997. [87] S.Ge,F.Hong,andT.Lee,\Robustadaptivecontrolofnonlinearsystemswithunknowntimedelays,"Automatica,vol.41,no.7,pp.1181{1190,Jul.2005. [88] S.Zhou,G.Feng,andS.Nguang,\Commentson"robuststabilizationofaclassoftime-delaynonlinearsystems,"IEEETrans.Autom.Control,vol.47,no.9,2002. [89] S.J.Yoo,J.B.Park,andY.H.Choi,\Commentson"adaptiveneuralcontrolforaclassofnonlinearlyparametrictime-delaysystems","IEEETrans.NeuralNetworks,vol.19,no.8,pp.1496{1498,Aug.2008. [90] D.Ho,J.Li,andY.Niu,\Replyto"commentson"adaptiveneuralcontrolforaclassofnonlinearlyparametrictime-delaysystems"","IEEETrans.NeuralNetworks,vol.19,no.8,pp.1498{1498,Aug.2008. [91] K.ShyuandJ.Yan,\Robuststabilityofuncertaintime-delaysystemsanditsstabilizationbyvariablestructurecontrol,"Int.J.Control,vol.57,no.1,pp.237{246,1993. [92] F.Gouaisbaut,W.Perruquetti,andJ.P.Richard,\Aslidingmodecontrolforlinearsystemswithinputandstatedelays,"inProc.IEEEConf.Decis.Control,vol.4,Dec.1999,pp.4234{4239. [93] F.Gouaisbaut,M.Dambrine,andJ.P.Richard,\SlidingmodecontrolofTDSviafunctionalsurfaces,"inProc.IEEEConf.Decis.Control,vol.5,Dec.2001,pp.4630{4634. 149

PAGE 150

F.Gouaisbaut,M.Dambrine,andJ.Richard,\Robustcontrolofdelaysystems:aslidingmodecontroldesignviaLMI,"Syst.Contr.Lett.,vol.46,no.4,pp.219{230,2002. [95] B.Mirkin,P.Gutman,andY.Shtessel,\Continuousmodelreferenceadaptivecontrolwithslidingmodeforaclassofnonlinearplantswithunknownstatedelay,"inProc.IEEEAm.ControlConf.,2009,pp.574{579. [96] Z.QuandJ.X.Xu,\Model-basedlearningcontrolsandtheircomparisonsusingLyapunovdirectmethod,"AsianJ.Control,vol.4(1),pp.99{110,2002. [97] M.FerrarinandA.Pedotti,\Therelationshipbetweenelectricalstimulusandjointtorque:Adynamicmodel,"IEEETrans.Rehabil.Eng.,vol.8,no.3,pp.342{352,2000. [98] J.L.Krevolin,M.G.Pandy,andJ.C.Pearce,\Momentarmofthepatellartendoninthehumanknee,"J.Biomech.,vol.37,pp.785{788,2004. [99] W.L.Buford,Jr.,F.M.Ivey,Jr.,J.D.Malone,R.M.Patterson,G.L.Peare,D.K.Nguyen,andA.A.Stewart,\Musclebalanceattheknee-momentarmsforthenormalkneeandtheACL-minusknee,"IEEETrans.Rehabil.Eng.,vol.5,no.4,pp.367{379,1997. [100] O.M.RutherfordandD.A.Jones,\Measurementofbrepennationusingultrasoundinthehumanquadricepsinvivo,"Eur.J.Appl.Physiol.,vol.65,pp.433{437,1992. [101] R.NathanandM.Tavi,\Theinuenceofstimulationpulsefrequencyonthegenerationofjointmomentsintheupperlimb,"IEEETrans.Biomed.Eng.,vol.37,pp.317{322,1990. [102] T.Watanabe,R.Futami,N.Hoshimiya,andY.Handa,\Anapproachtoamusclemodelwithastimulusfrequency-forcerelationshipforFESapplications,"IEEETrans.Rehabil.Eng.,vol.7,no.1,pp.12{17,1999. [103] A.Filippov,\Dierentialequationswithdiscontinuousright-handside,"Am.Math.Soc.Transl.,vol.42,pp.199{231,1964. [104] ||,Dierentialequationswithdiscontinuousright-handside.Netherlands:KluwerAcademicPublishers,1988. [105] G.V.Smirnov,Introductiontothetheoryofdierentialinclusions.AmericanMathematicalSociety,2002. [106] J.P.AubinandH.Frankowska,Set-valuedanalysis.Birkhuser,2008. [107] F.H.Clarke,Optimizationandnonsmoothanalysis.SIAM,1990. 150

PAGE 151

B.PadenandS.Sastry,\AcalculusforcomputingFilippov'sdierentialinclusionwithapplicationtothevariablestructurecontrolofrobotmanipulators,"IEEETrans.CircuitsSyst.,vol.34,pp.73{82,1987. [109] D.ShevitzandB.Paden,\Lyapunovstabilitytheoryofnonsmoothsystems,"IEEETrans.Autom.Control,vol.39,pp.1910{1914,1994. [110] R.RienerandJ.Quintern,BiomechanicsandNeuralControlofPostureandmovement,J.WintersandP.E.Crago,Eds.Springer-VerlagNewYork,Inc,2000. [111] J.HausdorandW.Durfee,\Open-looppositioncontrolofthekneejointusingelectricalstimulationofthequadricepsandhamstrings,"Med.Biol.Eng.Comput.,vol.29,pp.269{280,1991. [112] F.L.Lewis,R.Selmic,andJ.Campos,Neuro-FuzzyControlofIndustrialSystemswithActuatorNonlinearities.Philadelphia,PA,USA:SocietyforIndustrialandAppliedMathematics,2002. [113] F.L.Lewis,D.M.Dawson,andC.Abdallah,RobotManipulatorControlTheoryandPractice.CRC,2003. [114] B.Xian,M.deQueiroz,andD.Dawson,\Acontinuouscontrolmechanismforuncertainnonlinearsystems,"inOptimalControl,StabilizationandNonsmoothAnalysis,ser.LectureNotesinControlandInformationSciences.Heidelberg,Germany:Springer,2004,vol.301,pp.251{264. [115] C.Makkar,G.Hu,W.G.Sawyer,andW.E.Dixon,\Lyapunov-basedtrackingcontrolinthepresenceofuncertainnonlinearparameterizablefriction,"IEEETrans.Autom.Control,vol.52,no.10,pp.1988{1994,2007. [116] N.Sharma,K.Stegath,C.M.Gregory,andW.E.Dixon,\Nonlinearneuromuscularelectricalstimulationtrackingcontrolofahumanlimb,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.17,no.6,pp.576{584,Dec.2009. [117] N.Sharma,C.M.Gregory,M.Johnson,andW.E.Dixon,\Modiedneuralnetwork-basedelectricalstimulationforhumanlimbtracking,"inProc.IEEEInt.Symp.Intell.Control,Sep.2008,pp.1320{1325. [118] C.M.Gregory,W.Dixon,andC.S.Bickel,\Impactofvaryingpulsefrequencyanddurationonmuscletorqueproductionandfatigue,"MuscleandNerve,vol.35,no.4,pp.504{509,2007. [119] F.Zajac,\Muscleandtendon:properties,models,scaling,andapplicationtobiomechanicsandmotorcontrol,"Crit.Rev.Biomed.Eng.,vol.17,no.4,pp.359{411,1989. [120] H.Hatze,\Amyocyberneticcontrolmodelofskeletalmuscle,"BiologicalCybernet-ics,vol.25,no.2,pp.103{119,1977. 151

PAGE 152

R.Happee,\Inversedynamicoptimizationincludinmusculardynamics,anewsimulationmethodappliedtogoaldirectedmovements,"J.Biomech.,vol.27,no.7,pp.953{960,1994. [122] F.L.Lewis,\Neuralnetworkcontrolofrobotmanipulators,"IEEEExpert,vol.11,no.3,pp.64{75,1996. [123] M.Ferrarin,F.Palazzo,R.Riener,andJ.Quintern,\Model-basedcontrolofFES-inducedsinglejointmovements,"IEEETrans.NeuralSyst.Rehabil.Eng.,vol.9,no.3,pp.245{257,Sep.2001. [124] N.Stefanovic,M.Ding,andL.Pavel,\AnapplicationofL2nonlinearcontrolandgainschedulingtoerbiumdopedberampliers,"ControlEng.Pract.,vol.15,pp.1107{1117,2007. [125] T.Fujinaka,Y.Kishida,M.Yoshioka,andS.Omatu,\Stabilizationofdoubleinvertedpendulumwithself-tuningneuro-PID,"inProc.IEEE-INNS-ENNSInt.JointConf.NeuralNetw.,vol.4,24{27July2000,pp.345{348. [126] F.Nagata,K.Kuribayashi,K.Kiguchi,andK.Watanabe,\Simulationofnegaintuningusinggeneticalgorithmsformodel-basedroboticservocontrollers,"inProc.Int.Sym.Comput.Intell.Robot.Autom.,20-23June2007,pp.196{201. [127] N.J.KillingsworthandM.Krstic,\PIDtuningusingextremumseeking:online,model-freeperformanceoptimization,"IEEEContr.Syst.Mag.,vol.26,no.1,pp.70{79,2006. [128] R.Kelly,V.Santibanez,andA.Loria,ControlofRobotManipulatorsinJointSpace.Springer,2005. [129] R.Anderson,M.Spong,andN.SandiaNationalLabs.,Albuquerque,\Bilateralcontrolofteleoperatorswithtimedelay,"IEEETrans.Autom.Control,vol.34,no.5,pp.494{501,1989. [130] G.Liu,J.Mu,D.Rees,andS.Chai,\DesignandstabilityanalysisofnetworkedcontrolsystemswithrandomcommunicationtimedelayusingthemodiedMPC,"Int.J.Control,vol.79,no.4,pp.288{297,2006. [131] T.Cameron,G.Loeb,R.Peck,J.Schulman,P.Strojnik,andP.Troyk,\Micromodularimplantstoprovideelectricalstimulationofparalyzedmusclesandlimbs,"IEEETrans.onBiomed.Eng.,vol.44,no.9,pp.781{790,1997. 152

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NitinSharmawasborninNovember1981inAmritsar,India.HereceivedhisBachelorofEngineeringdegreeinindustrialengineeringfromThaparUniversity,India.Afterhisgraduationin2004,hewashiredasagraduateengineertraineefrom2004to2005andworkedasanexecutiveengineerfrom2005to2006inMarutiSuzukiIndiaLtd.HethenjoinedtheNonlinearControlsandRobotics(NCR)researchgrouptopursuehisdoctoralresearchundertheadvisementofDr.WarrenE.Dixon.HewillbejoiningasapostdoctoralfellowinDr.RichardStein'slaboratoryattheUniversityofAlberta,Edmonton,Canada. 153