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Nonlinear Control of Linear Parameter Varying Systems with Applications to Hypersonic Vehicles

Permanent Link: http://ufdc.ufl.edu/UFE0041960/00001

Material Information

Title: Nonlinear Control of Linear Parameter Varying Systems with Applications to Hypersonic Vehicles
Physical Description: 1 online resource (94 p.)
Language: english
Creator: Wilcox, Zachary
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: aerospace, aircraft, controls, hypersonic, linear, lpv, nonlinear, parameter, robust, varying, vehicle
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Aerospace Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The focus of this dissertation is to design a controller for linear parameter varying (LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the interplay between control performance and the structural dynamics design. Specifically a Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded, nonvanishing disturbances. The hypersonic vehicle has time varying parameters, specifically temperature profiles, and its dynamics can be reduced to an LPV system with additive disturbances. Since the HSV can be modeled as an LPV system the proposed control design is directly applicable. The control performance is directly examined through simulations. A wide variety of applications exist that can be effectively modeled as LPV systems. In particular, flight systems have historically been modeled as LPV systems and associated control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs), linear fractional transformations (LFT), and ?-types. However, as the type of flight environments and trajectories become more demanding, the traditional LPV controllers may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in the dynamics. HSV flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics, the aerothermoelastic effects are modeled by a temperature dependent, parameter varying state-space representation with added disturbances. The model includes an uncertain parameter varying state matrix, an uncertain parameter varying non-square (column deficient) input matrix, and an additive bounded disturbance. In this dissertation, a robust dynamic controller is formulated for a uncertain and disturbed LPV system. The developed controller is then applied to a HSV model, and a Lyapunov analysis is used to prove global exponential reference model tracking in the presence of uncertainty in the state and input matrices and exogenous disturbances. Simulations with a spectrum of gains and temperature profiles on the full nonlinear dynamic model of the HSV is used to illustrate the performance and robustness of the developed controller. In addition, this work considers how the performance of the developed controller varies over a wide variety of control gains and temperature profiles and are optimized with respect to different performance metrics. Specifically, various temperature profile models and related nonlinear temperature dependent disturbances are used to characterize the relative control performance and effort for each model. Examining such metrics as a function of temperature provides a potential inroad to examine the interplay between structural/thermal protection design and control development and has application for future HSV design and control implementation.
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 Zachary Wilcox.
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: UFE0041960:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041960/00001

Material Information

Title: Nonlinear Control of Linear Parameter Varying Systems with Applications to Hypersonic Vehicles
Physical Description: 1 online resource (94 p.)
Language: english
Creator: Wilcox, Zachary
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: aerospace, aircraft, controls, hypersonic, linear, lpv, nonlinear, parameter, robust, varying, vehicle
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Aerospace Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The focus of this dissertation is to design a controller for linear parameter varying (LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the interplay between control performance and the structural dynamics design. Specifically a Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded, nonvanishing disturbances. The hypersonic vehicle has time varying parameters, specifically temperature profiles, and its dynamics can be reduced to an LPV system with additive disturbances. Since the HSV can be modeled as an LPV system the proposed control design is directly applicable. The control performance is directly examined through simulations. A wide variety of applications exist that can be effectively modeled as LPV systems. In particular, flight systems have historically been modeled as LPV systems and associated control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs), linear fractional transformations (LFT), and ?-types. However, as the type of flight environments and trajectories become more demanding, the traditional LPV controllers may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in the dynamics. HSV flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics, the aerothermoelastic effects are modeled by a temperature dependent, parameter varying state-space representation with added disturbances. The model includes an uncertain parameter varying state matrix, an uncertain parameter varying non-square (column deficient) input matrix, and an additive bounded disturbance. In this dissertation, a robust dynamic controller is formulated for a uncertain and disturbed LPV system. The developed controller is then applied to a HSV model, and a Lyapunov analysis is used to prove global exponential reference model tracking in the presence of uncertainty in the state and input matrices and exogenous disturbances. Simulations with a spectrum of gains and temperature profiles on the full nonlinear dynamic model of the HSV is used to illustrate the performance and robustness of the developed controller. In addition, this work considers how the performance of the developed controller varies over a wide variety of control gains and temperature profiles and are optimized with respect to different performance metrics. Specifically, various temperature profile models and related nonlinear temperature dependent disturbances are used to characterize the relative control performance and effort for each model. Examining such metrics as a function of temperature provides a potential inroad to examine the interplay between structural/thermal protection design and control development and has application for future HSV design and control implementation.
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 Zachary Wilcox.
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: UFE0041960:00001


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NONLINEAR CONTROL OF LINEAR PARAMETER VARYING SYSTEMS WITH
APPLICATIONS TO HYPERSONIC VEHICLES



















By

ZACHARY DONALD WILCOX



















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





























2010 Zachary Donald Wilcox























This work is dedicated to my parents, family, friends, and advisor, who have provided me

with support during the challenging moments in this dissertation work.









ACKNOWLEDGMENTS

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

is a person with remarkable affability. As an advisor, he provided the necessary guidance

and allowed me to develop my own ideas. As a mentor, he helped me understand the

intricacies of working in a professional environment and helped develop my professional

skills. I feel fortunate in getting the opportunity to work with him.









TABLE OF CONTENTS
page

ACKNOWLEDGMENTS ..................... ............ 4

LIST OF TABLES ...................... ............... 7

LIST OF FIGURES ..................... ............... 8

A BSTRACT . . .... .. 10

CHAPTER

1 INTRODUCTION ..................... ............ 12

1.1 Motivation and Problem Statement ..................... .. .. 12
1.2 Outline and Contributions ................... ....... 16

2 LYAPUNOV-BASED EXPONENTIAL TRACKING CONTROL OF LPV SYS-
TEMS WITH AN UNKNOWN SYSTEM MATRIX, UNCERTAIN INPUT MA-
TRIX VIA DYNAMIC INVERSION ................... ..... 19

2.1 Introduction ...................... ............ 19
2.2 Linear Parameter Varying Model ..................... ... .. 21
2.3 Control Development .................... ......... 23
2.3.1 Control Objective .................... ........ 23
2.3.2 Open-Loop Error System ................... ..... 24
2.3.3 Closed-Loop Error System ................. ....... 25
2.4 Stability Analysis .................... ............ 27
2.5 Conclusions . ... 30

3 HYPERSONIC VEHICLE DYNAMICS AND TEMPERATURE MODEL ... 32

3.1 Introduction .................... .............. 32
3.2 Rigid Body and Elastic Dynamics ................. ....... 32
3.3 Temperature Profile Model ................... ....... 33
3.4 Conclusion ..................... ............... 38

4 LYAPUNOV-BASED EXPONENTIAL TRACKING CONTROL OF A HY-
PERSONIC AIRCRAFT WITH AEROTHERMOELASTIC EFFECTS ..... 39

4.1 Introduction .................... .............. 39
4.2 HSV Model ..................... .............. 41
4.3 Control Objective ..................... .......... 42
4.4 Simulation Results .................... ........... 44
4.5 Conclusion .................................... 48









5 CONTROL PERFORMANCE VARIATION DUE TO NONLINEAR AEROTHER-
MOELASTICITY IN A HYPERSONIC VEHICLE: INSIGHTS FOR STRUC-
TURAL DESIGN .................... .............. 53

5.1 Introduction .................... .............. 53
5.2 Dynamics and Controller .................... ........ 54
5.3 Optimization via Random Search and Evolving Algorithms ........ 55
5.4 Example Case .................... .............. 57
5.5 Results ..................... ................. 61
5.6 Conclusion ...................... .............. 73

6 CONCLUSIONS AND FUTURE WORK ......................... 75

6.1 Conclusions ..................... .............. 75
6.2 Contributions .................... .............. 76
6.3 Future W ork ..................... ............. 77

APPENDIX

A OPTIMIZATION DATA .................... .......... 79

REFERENCES ..................... ................. 89

BIOGRAPHICAL SKETCH .................... ............ 94









LIST OF TABLES


Table page

3-1 Natural frequencies for 5 linear temperature profiles (Nose/Tail) in degrees F.
Percent difference is the difference between the maximum and minimum fre-
quencies divided by the minimum frequency. ..... 36

5-1 Optimization Control Gain Search Statistics ..... 73

A-1 Total cost function, used to generate Figure 5-11 and 5-12 (Part 1) ...... ..80

A-2 Total cost function, used to generate Figure 5-11 and 5-12 (Part 2) ...... ..80

A-3 Control input cost function, used to generate Figure 5-7 and 5-8 (Part 1) 81

A-4 Control input cost function, used to generate Figure 5-7 and 5-8 (Part 2) 81

A-5 Error cost function, used to generate Figure 5-9 and 5-10 (Part 1) ... 82

A-6 Error cost function, used to generate Figure 5-9 and 5-10 (Part 2) ... 82

A-7 Pitch rate, peak-to-peak error, used to generate Figure 5-13 and 5-14 (Part 1) .83

A-8 Pitch rate, peak-to-peak error, used to generate Figure 5-13 and 5-14 (Part 2) .83

A-9 Pitch rate, steady-state peak-to-peak error, used to generate Figure 5-21 and 5-
22 (Part 1) ....................................... 84

A-10 Pitch rate, steady-state peak-to-peak error, used to generate Figure 5-21 and 5-
22 (Part 2) .... ................ ................ 84

A-11 Pitch rate, time to steady-state, used to generate Figure 5-17 and 5-18 (Part 1) 85

A-12 Pitch rate, time to steady-state, used to generate Figure 5-17 and 5-18 (Part 2) 85

A-13 Velocity, peak-to-peak error, used to generate Figure 5-15 and 5-16 (Part 1) .86

A-14 Velocity, peak-to-peak error, used to generate Figure 5-15 and 5-16 (Part 2) .86

A-15 Velocity, steady-state peak-to-peak, used to generate Figure 5-23 and 5-24 (Part
1) ......................................... 87

A-16 Velocity, steady-state peak-to-peak, used to generate Figure 5-23 and 5-24 (Part
2 ) . . 8 7

A-17 Velocity, time to steady-state, used to generate Figure 5-19 and 5-20 (Part 1) 88

A-18 Velocity, time to steady-state, used to generate Figure 5-19 and 5-20 (Part 2) 88









LIST OF FIGURES


Figure page

3-1 Modulus of elasticity for the first three dynamic modes of vibration for a free-
free beam of titanium ................... ............... 34

3-2 Frequencies of vibration for the first three dynamic modes of a free-free tita-
nium beam .................... ................. .. 35

3-3 Nine constant temperature sections of the HSV used for temperature profile
modeling. ......................................... 35

3-4 Linear temperature profiles used to calculate values shown in Table 3-1. 37

3-5 Asymetric mode shapes for the hypersonic vehicle. The percent difference was
calculated based on the maximum minus the minimum structural frequencies
divided by the minimum.. ............................ 37

4-1 Temperature variation for the forebody and aftbody of the hypersonic vehicle
as a function of time ................... ............. 45

4-2 In this figure, fi denotes the ith element in the disturbance vecor f. Disturbances
from top to bottom: velocity fy, angle of attack fa, pitch rate fQ, the 1st elas-
tic structural mode i)1, the 2nd elastic structural mode i)2, and the 3rd elastic
structural mode i)3, as described in (4-11). ..... .. 46

4-3 Reference model ouputs y,, which are the desired trajectories for top: velocity
Vm (t), middle: angle of attack a, (t), and bottom: pitch rate Qm (t). 47

4-4 Top: velocity V (t), bottom: velocity tracking error ev (t). .. 48

4-5 Top: angle of attack a (t), bottom: angle of attack tracking error e (t). 49

4-6 Top: pitch rate Q (t), bottom: pitch rate tracking error CQ (t) .. 49

4-7 Top: fuel equivalence ratio f. Middle: elevator deflection 6,. Bottom: Canard
deflection 6. .. ............... ..................... 50

4-8 Top: altitude h (t), bottom: pitch angle 0 (t) . 50

4-9 Top: 1st structural elastic mode p7. Middle: 2nd structural elastic mode rl2. Bot-
tom: 3rd structural elastic mode r3. . .. 51

5-1 HSV surface temperature profiles. T0os, E [450F, 900F], and Ttail E [100F, 800F]. 54

5-2 Desired trajectories: pitch rate Q (top) and velocity V (bottom). ... 58

5-3 Disturbances for velocity V (top), angle of attack a (second from top), pitch
rate Q (second from bottom) and the 1st structural mode (bottom). ...... ..58









5-4 Tracking errors for the pitch rate Q in degrees/sec (top) and the velocity V in
ft/sec (bottom)....................... .............. 59

5-5 Control inputs for the elevator 6, in degrees (top) and the fuel ratio Of (bottom). 60

5-6 Cost function values for the total cost Qtot (top), the input cost ,co, (middle)
and the error cost err, (bottom). ......................... .. 60

5-7 Control cost function ,co, data as a function of tail and nose temperature pro-
files. ...................................... ... .. 62

5-8 Control cost function Qco, data (filtered) as a function of tail and nose temper-
ature profiles .................... ................ .. 62

5-9 Error cost function Q er data as a function of tail and nose temperature profiles. 63

5-10 Error cost function Q er, data (filtered) as a function of tail and nose tempera-
ture profiles .. . . ..... 63

5-11 Total cost function Qtot data as a function of tail and nose temperature profiles. 64

5-12 Total cost function Qtot data (filtered) as a function of tail and nose tempera-
ture profiles .. . . ..... 65

5-13 Peak-to-peak transient error for the pitch rate Q (t) tracking error in deg./sec.. 66

5-14 Peak-to-peak transient error (filtered) for the pitch rate Q (t) tracking error in
deg./sec.. ..................... .................. 66

5-15 Peak-to-peak transient error for the velocity V (t) tracking error in ft/sec.. ... 67

5-16 Peak-to-peak transient error (filtered) for the velocity V (t) tracking error in
ft./sec.. .................... ................ 67

5-17 Time to steady-state for the pitch rate Q (t) tracking error in seconds. ....... 68

5-18 Time to steady-state (filtered) for the pitch rate Q (t) tracking error in seconds. 68

5-19 Time to steady-state for the velocity V (t) tracking error in seconds. ...... ..69

5-20 Time to steady-state (filtered) for the velocity V (t) tracking error in seconds. .69

5-21 Steady-state peak-to-peak error for the pitch rate Q (t) in deg./sec.. ...... 70

5-22 Steady-state peak-to-peak error (filtered) for the pitch rate Q (t) in deg./sec.. 71

5-23 Steady-state peak-to-peak error for the velocity V (t) in ft./sec.. ... 71

5-24 Steady-state peak-to-peak error (filtered) for the velocity V (t) in ft./sec. 72

5-25 Combined optimization p chart of the control and error costs, transient and
steady-state values.... ................ ............. .. 73









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

NONLINEAR CONTROL OF LINEAR PARAMETER VARYING SYSTEMS WITH
APPLICATIONS TO HYPERSONIC VEHICLES

By

Zachary Donald Wilcox

August 2010

Chair: Warren E. Dixon
Major: Aerospace Engineering

The focus of this dissertation is to design a controller for linear parameter varying

(LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the

interplay between control performance and the structural dynamics design. Specifically a

Lyapunov-based continuous robust controller is developed that yields exponential tracking

of a reference model, despite the presence of bounded, nonvanishing disturbances. The

hypersonic vehicle has time varying parameters, specifically temperature profiles, and its

dynamics can be reduced to an LPV system with additive disturbances. Since the HSV

can be modeled as an LPV system the proposed control design is directly applicable. The

control performance is directly examined through simulations.

A wide variety of applications exist that can be effectively modeled as LPV systems.

In particular, flight systems have historically been modeled as LPV systems and associated

control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs),

linear fractional transformations (LFT), and /-types. However, as the type of flight

environments and trajectories become more demanding, the traditional LPV controllers

may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an

inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in

the dynamics. HSV flight conditions produce temperature variations that can alter both

the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics,

the aerothermoelastic effects are modeled by a temperature dependent, parameter varying









state-space representation with added disturbances. The model includes an uncertain

parameter varying state matrix, an uncertain parameter varying non-square (column

deficient) input matrix, and an additive bounded disturbance. In this dissertation, a

robust dynamic controller is formulated for a uncertain and disturbed LPV system. The

developed controller is then applied to a HSV model, and a Lyapunov analysis is used to

prove global exponential reference model tracking in the presence of uncertainty in the

state and input matrices and exogenous disturbances. Simulations with a spectrum of

gains and temperature profiles on the full nonlinear dynamic model of the HSV is used to

illustrate the performance and robustness of the developed controller.

In addition, this work considers how the performance of the developed controller

varies over a wide variety of control gains and temperature profiles and are optimized

with respect to different performance metrics. Specifically, various temperature profile

models and related nonlinear temperature dependent disturbances are used to characterize

the relative control performance and effort for each model. Examining such metrics as

a function of temperature provides a potential inroad to examine the interplay between

structural/thermal protection design and control development and has application for

future HSV design and control implementation.









CHAPTER 1
INTRODUCTION

1.1 Motivation and Problem Statement

Recent research on nonlinear inversion of the input dynamics based on Lyapunov

stability theory provides a stepping stone to LPV dynamic inversion. In [27, 28], dynamic

inversion techniques are used to design controllers that can adaptively and robustly

stabilize state-space systems with uncertain constant parameters and additive unknown

bounded disturbances. However, this work is limited to time-invarient parameters and

therefore is not applicable to LPV systems. The work presented in this chapter is an

extension of the work in [27, 28], and provides a continuous robust controller that is able

to stabilize general perturbed LPV systems with disturbances, when both the state, input

matrices, time-varying parameters, and disturbances are unknown.

The design of guidance and control systems for airbreathing HSV is challeng-

ing because the dynamics of the HSV are complex and highly coupled as in [10], and

temperature-induced stiffness variations impact the structural dynamics such as in [21].

Much of this difficulty arises from the aerodynamic, thermodynamic, and elastic coupling

(aerothermoelasticity) inherent in HSV systems. Because HSV travel at such high veloc-

ities (in excess of Mach 5) there are large amounts of aerothermal heating. Aerothermal

heating is non-uniform, generally producing much higher temperatures at the stagnation

point of airflow near the front of the vehicle. Coupled with additional heating due to

the engine, HSVs have large thermal gradients between the nose and tail. The structural

dynamics, in turn, affect the aerodynamic properties. Vibration in the forward fuselage

changes the apparent turn angle of the flow, which results in changes in the pressure

distribution over the forebody of the aircraft. The resulting changes in the pressure dis-

tribution over the aircraft manifest themselves as thrust, lift, drag, and pitching moment

perturbations as in [10]. To develop control laws for the longitudinal dynamics of a HSV









capable of compensating for these structural and aerothermoelastic effects, structural

temperature variations and structural dynamics must be considered.

Aerothermoelasticity is the response of elastic structures to aerodynamic heating and

loading. Aerothermoelastic effects cannot be ignored in hypersonic flight, because such ef-

fects can destabilize the HSV system as in [21]. A loss of stiffness induced by aerodynamic

heating has been shown to potentially induce dynamic instability in supersonic/hypersonic

flight speed regimes as in [1]. Yet active control can be used to expand the flutter bound-

ary and convert unstable limit cycle oscillations (LCO) to stable LCO as shown in [1]. An

active structural controller was developed in [26], which accounts for variations in the HSV

structural properties resulting from aerothermoelastic effects. The control design in [26]

models the structural dynamics using a LPV framework, and states the benefits to using

the LPV framework are two-fold: the dynamics can be represented as a single model, and

controllers can be designed that have affine dependency on the operating parameters.

Previous publications have examined the challenges associated with the control

of HSVs. For example, HSV flight controllers are designed using genetic algorithms to

search a design parameter space where the nonlinear longitudinal equations of motion

contain uncertain parameters as in [4, 30, 49]. Some of these designs utilize Monte Carlo

simulations to estimate system robustness at each search iteration. Another approach

[4] is to use fuzzy logic to control the attitude of the HSV about a single low end flight

condition. While such approaches as in [4, 30, 49] generate stabilizing controllers, the

procedures are computationally demanding and require multiple evaluation simulations

of the objective function and have large convergent times. An adaptive gain-scheduled

controller in [55] was designed using estimates of the scheduled parameters, and a semi-

optimal controller is developed to adaptively attain Ho control performance. This

controller yields uniformly bounded stability due to the effects of approximation errors

and algorithmic errors in the neural networks. Feedback linearization techniques have

been applied to a control-oriented HSV model to design a nonlinear controller as in [32].









The model in [32] is based on a previously developed HSV longitudinal dynamic model

in [8]. The control design in [32] neglects variations in thrust lift parameters, altitude,

and dynamic pressure. Linear output feedback tracking control methods have been

developed in [44], where sensor placement strategies can be used to increase observability,

or reconstruct full state information for a state-feedback controller. A robust output

feedback technique is also developed for the linear parameterizable HSV model, which

does not rely on state observation. A robust setpoint regulation controller in [17] is

designed to yield asymptotic regulation in the presence of parametric and structural

uncertainty in a linear parameterizable HSV system.

An adaptive controller in [19] was designed to handle (linear in the parameters)

modeling uncertainties, actuator failures, and non-minimum phase dynamics as in [17]

for a HSV with elevator and fuel ratio inputs. Another adaptive approach in [41] was

recently developed with the addition of a guidance law that maintains the fuel ratio

within its choking limits. While adaptive control and guidance control strategies for a

HSV are investigated in [17, 19, 41], neither addresses the case where dynamics include

unknown and unmodeled disturbances. There remains a need for a continuous controller,

which is capable of achieving exponential tracking for a HSV dynamic model containing

aerothermoelastic effects and unmodeled disturbances (i.e., nonvanishing disturbances that

do not satisfy the linear in the parameters assumption).

In the context of the aforementioned literature, a contribution of this dissertation

(and in the publications in [51] and [52]) is the development of a controller that achieves

exponential model reference output tracking despite an uncertain model of the HSV

that includes nonvanishing exogenous disturbances. A nonlinear temperature-dependent

parameter-varying state-space representation is used to capture the aerothermoelastic ef-

fects and unmodeled uncertainties in a HSV. This model includes an unknown parameter-

varying state matrix, an uncertain parameter-varying non-square (column deficient) input

matrix, and a nonlinear additive bounded disturbance. To achieve an exponential tracking









result in light of these disturbances, a robust, continuous Lyapunov-based controller is

developed that includes a novel implicit learning characteristic that compensates for the

nonvanishing exogenous disturbance. That is, the use of the implicit learning method

enables the first exponential tracking result by a continuous controller in the presence of

the bounded nonvanishing exogenous disturbance. To illustrate the performance of the

developed controller, simulations are performed on the full nonlinear model given in [10]

that includes aerothermoelastic model uncertainties and nonlinear exogenous disturbances

whose magnitude is based on airspeed fluctuations.

In addition to the control development, there exists the need to understand the

interplay of a control design with respect to the vehicle dynamics. A previous control

oriented design analysis in [6] states that simultaneously optimizing both the structural

dynamics and control is an intractable problem, but that control-oriented design may

be performed by considering the closed-loop performance of an optimal controller on a

series of different open-loop design models. The best performing design model is then said

to have the optimal dynamics in the sense of controllability. Knowledge of the optimal

thermal gradients will provide insight to engineers on how to properly weight the HSV's

thermal protection system for both steady-state and transient flight. The preliminary

work by authors in [6] provides a control-oriented design architecture by investigating

control performance variations due to thermal gradients using an Hoo controller. Chapter

5 seeks to extend the control oriented design concept to examine control performance

variations for HSV models that include nonlinear aerothermoelastic disturbances. Given

these disturbances, Chapter 5 focuses on examining control performance variations for

the model reference robust controller in Chapter 2 and Chapter 4 to achieve a nonlinear

control-oriented analysis with respect to thermal gradients on the HSV. By analyzing

control error and input norms as well as transient and steady-state responses over a wide

range of temperature profiles an optimal temperature profile range is suggested.









1.2 Outline and Contributions

This dissertation focuses on designing a nonlinear controller for general disturbed

LPV system. The controller is then modified for a specific air-breathing HSV. The

dynamic inversion design is a technique that allows the multiplicative input matrices to

be inverted, thus rendering the controller affine in the control. Previous results in [27] and

[29] have examined full state and output feedback adaptive dynamic inversion controllers,

but are limited because they contain constant uncertainties. The HSV system presents

a new challenge because the uncertain state and input matrices are parameter varying.

Specifically, the state and input matrices of the hypersonic vehicle vary as a function of

temperature. This chapter provides some background and motivates the robust dynamic

inversion control method subsequently developed. A brief outline of the following chapters

follows.

In Chapter 2 a tracking controller is presented that achieves exponential stability of

a model reference system in the presence of uncertainties and disturbances. Specifically,

the plant model contains time-varying parametric uncertainty with disturbances that are

bounded and nonvanishing. The contribution of this result is that it represents the first

ever development of an exponentially stable continuous robust model reference tracking

controller for an LPV system with an unknown system matrix and uncertain input matrix

with an additive unknown bounded disturbance. Lyapunov based methods are used to

prove exponential stability of the system.

Chapter 3 provides the nonlinear dynamics and temperature model of a HSV. The

nonlinear and highly coupled dynamic equations are presented. The equations that

define the aerodynamic and generalized moments and forces are provided explicitly in

previous literature. This chapter is meant to serve as an overview of the dynamics of the

HSV. In addition to the flight and structural dynamics, temperature profile modeling is

provided. Temperature variations impact the HSV flight dynamics through changes in the

structural dynamics which affect the mode shapes and natural frequencies of the vehicle.









The presented model offers an approximate approach, whereby the natural frequencies

of a continuous beam are described as a function of the mass distribution of a beam

and its stiffness. Figures and tables are presented to emphasize the need to include such

dynamics for control design. This chapter is designed to familiarize the reader with the

HSV dynamic and temperature models, since these dynamics are used throughout this

dissertation. This chapter is a precursor and introduction to Chapter 4 and Chapter 5.

Using the controller developed in Chapter 2, the contribution in Chapter 4 is to

illustrate an application to an air-breathing hypersonic vehicle system with additive

bounded disturbances and aerothermoelastic effects, where the control input is multiplied

by an uncertain, column deficient, parameter-varying matrix. In addition to the stability

proof, the control design is also validated through implementation in a full nonlinear

dynamic simulation. The exogenous disturbances (e.g., wind gust, engine variations, etc.)

and temperature profiles (aerodynamic driven thermal heating) are designed to examine

the robustness of the developed controller. The results from the simulation illustrate the

boundedness of the controller with favorable transient and steady state tracking errors and

provide evidence that the control model used for development is valid.

The contribution in Chapter 5 is to provide an analysis framework to examine the

nonlinear control performance based on variations in the vehicle dynamics. Specifically,

the changes occur in the structural dynamics via their response to different temperature

profiles, and hence the observed vibration has different frequencies and shapes. Using

an initial random search and evolving algorithms, approximate optimal gains are found

for the controller for each temperature dependant plant model. Errors, control effort,

transient and steady-state performance analysis is provided. The results from this analysis

show that there is a temperature range for operation of the HSV that minimizes a given

cost of performance versus control authority. Knowledge of a favorable range with regard

to control performance provides designers an extra tool when developing the thermal

protection system as well as the structural characteristics of the HSV.









Chapter 6 summarizes the contributions of the dissertation and possible avenues for

future work are provided. The brief contributions of the LPV controller, HSV example

controller design application, and the HSV optimization procedure provide the base of this

dissertation. After a brief summary, some of the drawbacks of the current control design

are presented as directions for future research work.









CHAPTER 2
LYAPUNOV-BASED EXPONENTIAL TRACKING CONTROL OF LPV SYSTEMS
WITH AN UNKNOWN SYSTEM MATRIX, UNCERTAIN INPUT MATRIX VIA
DYNAMIC INVERSION

2.1 Introduction

Linear parameter varying (LPV) systems have a wide range of practical engineering

applications. Some examples include several missile autopilot designs as in [7, 39, 43],

a turbofan engine [5], and active suspension design [18]. Traditionally, LPV systems

have been developed using a gain scheduling control approach. Gain scheduling is a

technique to develop controllers for nonlinear system using traditional linear control

theory. Gain scheduling is a technique where the system is linearized about certain

operating conditions. About these operating conditions, constant parameters are assumed

and separate control schemes and gains are chosen. More than a decade ago, Shamma et.

al. pointed out some of the potential hazards of gain scheduling in [42]. In particular, gain

scheduling is a analytically non-continuous method and stability is not guaranteed while

switching from one region of linearization to another. In fact the two biggest downfalls of

gain scheduling control design is the linearization of the plant models close to equilibrium

or constant parameters states and the requirement that the parameters must change

slowly. Because the linearization is required to be close to some operation condition

or stability point, many different schedules have to be taken. And by requiring that

parameters change slowly, the gain scheduling techniques are not appropriate for many

quickly varying systems.

Another approach to LPV problems is the use of linear matrix inequalities (LMIs).

In a book on LMIs and their use in system and control theory in [11], Boyd et. al. states

that LMIs are mathematically convex optimization problems with extensions to control

theory. However in [11] it is pointed out that these typically require numerical solutions

and there are only a few special cases where analytical solutions exist. These LPV

solutions typically only provide norm based solutions. The most common of these is the









L2-norm because it allows for continuity with Ho, control when the systems become linear

time-invariant. For instance H, control is developed in [14] which uses LMIs to optimize

the solution and in [3], the parameterization of LMIs was investigated in the context of

control theory. H, control is developed in [14], which uses LMIs to optimize the solution

and Saif et. al. in [48] shows that stabilization solutions exist for multi-input-multi-output

(i\ li\ O) systems using LMIs. These designs allow for the continuous solution of LPV

systems, however knowledge of the structure of the system must be known, and the

parameters are assumed measurable online. In [25] minimax controllers are designed to

handle only constant or small variations in the parameters, where the parameterized

algebraic Riccati inequalities are converted into equivalent LMIs so that the convexity

can be exploited and a controller developed. Continuous control design for uncertain LPV

systems in [13] is designed using LMIs, however the procedure is limited to uncertainties in

the state matrix, and does not cover uncertainties in the input matrix.

Another approach uses linear fractional transformations LFTs in the context of LPV

control design such as in [31] and are based on small gain theory. This approach cannot

handle uncertain parameters. However, by extending the solution in [31] the design can

include uncertain parameters which are not available to the controller. These solutions

are /-synthesis type controllers, however the solvability conditions are non-convex and

therefore a solution to the problem is not guaranteed even when a stable controller exists.

Several examples of recursive /-type solutions are given in [2, 22, 45]. More recently in

[26], the /-type solutions have been extended to a hypersonic aircraft example, but suffers

the same non-convexity problem as the formerly listed /-type literature.

Recent research on nonlinear inversion of the input dynamics based on Lyapunov

stability theory provides a stepping stone to LPV dynamic inversion. In [27, 28], dynamic

inversion techniques are used to design controllers that can adaptively and robustly

stabilize a more general state-space system that has been considered in previous work with

uncertain constant parameters and additive unknown bounded disturbances. However,









this work is limited to time-invarient parameters and therefore is not applicable to LPV

systems. The work presented in this chapter is an extension of the work in [27, 28], and

provides a continuous robust controller that is able to exponentially stabilize LPV systems

with unknown bounded disturbances, when both the state, input matrices, time-varying

parameters, and disturbances are unknown.

2.2 Linear Parameter Varying Model

The dynamic model used for the subsequent control development is a combination of

linear-parameter-varying (LPV) system with an added unmodeled disturbance as


S=A(p(t))x+B(p(t))u+f(t) (2-1)

y = Cx. (2-2)


In (2-1) and (2-2), x (t) E R' is the state vector, A (p (t)) E IR"' denotes a linear

parameter varying state matrix, B (p (t)) E R x denotes a linear parameter varying

input matrix, C IRqxn denotes a known output matrix, u(t) E RP denotes control

vector, p (t) represents the unknown time-dependent parameters, f(t) E IR' represents a

time-dependent unknown, nonlinear disturbance, and y (t) E IRq represents the measured

output vector. The subsequent control development is based on the assumption that

p > q, meaning that at least one control input is available for each output state. When the

system is overactuated in that there are more control inputs available than output states,

then p > q and the resulting input dynamic inversion matrix will be row deficient. For

this case, a right pseudo-inverse can be used in conjunction with a singularity avoidance

law. For instance, if a ]Rqxp then the pseudo-inverse a+ = (T (TT)-1 and satisfies

(aT+ = Iqxq where Iqxq is an identity matrix of dimension q x q.

The matrices A (p (t)) and B (p (t)) have the standard linear parameter-varying form


A(p,t) = Ao + E (p(t))A (2-3)
i=

B (p,t) =Bo + ((t))B, (2-4)
i=1









where Ao e R'x" and Bo E represent known nominal matrices with unknown

variations (p (t)) Ai and (p (t)) Bi for i = 1,2,..., s, where Ai E IRx" and Bi IRfxp

are time-invariant matrices, and (p (t)) ,' (p (t)) IR are parameter-dependent

weighting terms. Knowledge of the nominal matrix Bo will be exploited in the subsequent

control design.

To facilitate the subsequent control design, a reference model is given as

xm = Ax, + B,6 (2-5)

Ym = Cxm (2-6)

where A, IR"x" and Bm IERxp denote the state and input matrices, respectively, where

A, is Hurwitz, 6 (t) E RP is a vector of reference inputs, y, (t) E IRq are the reference
outputs, and C was defined in (2-2).

Assumption 1: The nonlinear disturbance f (t) and its first two time derivatives are

assumed to exist and be bounded by known constants.

Assumption 2: The dynamics in (2-1) are assumed to be controllable.

Assumption 3: The matrices A (p (t)) and B (p (t)) and their time derivatives satisfy

the following inequalities:


IIA(p (t))|
A(p (t)) < (Ad Bp(Pt)) < (Bd

where (A, (B, (Ad, (Bd E R+ are known bounding constants, and I| denotes the induced

infinity norm of a matrix. As is typical in robust control methods, knowledge of the upper

bounds in (2-7) are used to develop sufficient conditions on gains used in the subsequent

control design.









2.3 Control Development


2.3.1 Control Objective
The control objective is to ensure that the output y(t) tracks the time-varying output
generated from the reference model in (2-5) and (2-6). To quantify the control objective,
an output tracking error, denoted by e (t) E IR, is defined as

e y m = C (x m). (2-8)

To facilitate the subsequent analysis, a filtered tracking error denoted by r (t) E IR, is
defined as

r e + (2-9)

where 7 IR2 is a positive definite diagonal, constant control gain matrix, and is se-
lected to place a relative weight on the error state verses its derivative. To facilitate the
subsequent robust control development, the state vector x(t) is expressed as


x (t) = x (t) + x (t) (2-10)

where x (t) E IR' contains the p output states, and x, (t) E IR' contains the remaining
n p states. Likewise, the reference states x,(t) can also be separated as in (2-10).
Assumption 4: The states contained in x,(t) in (2-10) and the corresponding time
derivatives can be further separated as

X" (t) = Xp (t) + X(, (t) (2-11)

Xu (t) = Xp (t) + MXu (t)

where X, (t) xp, (t) x, (t) (t) E R" are upper bounded as


I (t) |_< c \1 1 1 |x (t) II < P, (2-12)

I (t)ll C< I I I \\I M (t)ll < I( 0









where z(t) E R2q is defined as
T1
zA eT r rT (2-13)

and cl, c, (C (u CE IR are known non-negative bounding constants. The terms in (2-11)

and (2-12) are used to develop sufficient gain conditions for the subsequent robust control

design.

2.3.2 Open-Loop Error System

The open-loop tracking error dynamics can be developed by taking the time deriva-

tive of (2-9) and using the expressions in (2-1)-(2-6) as

r = e + 7e

= C ( m) + e

= C (Ax + A+. + B i + B + f (t) Amim BmK ) +

= N + Nd + CBu + CBi e. (2-14)

The auxiliary functions N (x, A, e, Xm, im, t) E iR and Nd (X ,m i, m t) e IR in (2 14)
are defined as

N A CA ( Im) + CA ( +,) + CAip,, + CAx, + + e (2-15)

and

Nd A Cf (t) + CAJI, + CAx(, + CAjm + CAx. CAmim CBm6. (2-16)

Motivation for the selective grouping of the terms in (2-15) and (2-16) is derived from the

fact that the following inequalities can be developed [38, 54] as

N
where po, (Nd E IR+ are known bounding constants.









2.3.3 Closed-Loop Error System

Based on the expression in (2-14) and the subsequent stability analysis, the control
input is designed as

S= -k, (CBo)-1 [(ks + qxq) e (t) (k + qxq) e (0) + v (t)] (2-18)

where v (t) E IRq is an implicit learning law with an update rule given by

v (t) = k. u (t) | sgn (r (t)) + (k, + Iqxq) ye (t) + ksgn (r (t)) (2-19)

and kr Rpxp, k, ks, k y E Rqxq denote positive definite, diagonal constant control gain
matrices, Bo E P. 'is introduced in (2-4), sgn (-) denotes the standard signum function

where the function is applied to each element of the vector argument, and Iqxq denotes a

q x q identity matrix.
After substituting the time derivative of (2-18) into (2-14), the error dynamics can be

expressed as

Ir = + Nd (k, u (t)|| sgn (r (t)) + CBu (2-20)

Q (k, + Ipp) r (t) Qk sgn (r (t)) e

where the auxiliary matrix Q (p (t)) E Rqxq is defined as

( A CBk (CBo)-1 (2-21)

where Q (p (t)) can be separated into diagonal (i.e., A (p (t)) E IRqxq) and off-diagonal (i.e.,

A (p (t)) E IRqxq) components as
0 = A+ A. (2-22)

Assumption 5: The subsequent development is based on the assumption that the

uncertain matrix Q (p (t)) is diagonally dominant in the sense that

Amin (A)- I|| A | > e (2-23)









where e IR+ is a known constant. While this assumption cannot be validated for a

generic system, the condition can be checked (within some certainty tolerances) for a

specific system. Essentially, this condition indicates that the nominal value Bo must

remain within some bounded region of B. In practice, bounds on the variation of B should

be known, for a particular system under a set of operating conditions, and this bound can

be used to check the sufficient conditions given in (2-23).

Motivation for the structure of the controller in (2-18) and (2-19) comes from the

desire to develop a closed-loop error system to facilitate the subsequent Lyapunov-based

stability analysis. In particular, since the control input is premultiplied by the uncertain

matrix CB in (2-14), the term CBo1 is motivated to generate the relationship in (2-21)

so that if the diagonal dominance assumption (Assumption 5) is satisfied, then the control

can provide feedback to compensate for the disturbance terms. The bracketed terms in

(2-18) include the state feedback, an initial condition term, and the implicit learning term.

The implicit learning term v (t) is the generalized solution to (2-19). The structure of the

update law in (2-19) is motivated by the need to reject the exogenous disturbance terms.

Specifically, the update law is motivated by a sliding mode control strategy that can be

used to eliminate additive bounded disturbances. Unlike sliding mode control (which

is a discontinuous control method requiring infinite actuator bandwidth), the current

continuous control approach includes the integral of the sgn(.) function. This implicit

learning law is the key element that allows the controller to obtain an exponential stability

result despite the additive nonvanishing exogenous disturbance. Other results in literature

also have used the implicit learning structure include [33, 34, 35, 36, 37, 40].









Differential equations such as (2-24) and (2-25) have discontinuous right-hand sides

as


S (t) = k |Iu (t) || sgn (r (t)) + (k, + Ipxp) ye (t) + kysgn (r (t)) (2-24)

r = N + Nd Qk, Iu (t)ll sgn (r (t)) + CBu (ks + Ipxp) r (t) .ksgn (r (t)) e.

(2-25)

Let ffij (y, t) E R2p denote the right-hand side of (2-24) and (2-25). Since the subsequent

analysis requires that a solution exist for y = ffi~ (y, t), it is important to show the

existence of the generalized solution. The existence of Filippov's generalized solution

[15] can be established for (2-24) and (2-25). First, note that ffil (y, t) is continuous

except in the set {(y, t) r = 0}. Let F (y, t) be a compact, convex, upper semicontinuous

set-valued map that embeds the differential equation y = ffil (x, t) into the differential

inclusion y F (y, t). An absolute continuous solution exists to y = F (x, t) that is a

generalized solution to ? = fe i (x, t). A common choice [15] for F (y, t) that satisfies the

above conditions is the closed convex hull of ffi (y, t). A proof that this choice for F (y, t)

is upper semicontinuous is given in [20].

2.4 Stability Analysis

Theorem: The controller given in (2-18) and (2-19) ensures exponential tracking in

the sense that

\e(t) < I (0)|1 exp t) Vt [0, o), (2-26)

where A, e R+, provided the control gains k,, k,, and ky introduced in (2-18) are selected

according to the sufficient conditions

( Bd P ( Nd
min (ku) > min (k) > r} Amin (k) > "" (2-27)
E 4E min {7, E} e

where po and (Nd are introduced in (2-17), e is introduced in (2-23), (Bd E R+ is a

known positive constant, and Amin () denotes the minimum eigenvalue of the argument.

The bounding constants are conservative upper bounds on the maximum expected









values. The Lyapunov analysis indicates that the gains in (2-27) need to be selected
sufficiently large based on the bounds. Therefore, if the constants are chosen to be

conservative, then the sufficient gain conditions will be larger. Values for these gains could

be determined through a physical understanding of the system (within some conservative

% of uncertainty) and/or through numerical simulations.
Proof: Let VL (z, t) : IR2q X [0, oc) -- IR be a Lipschitz continuous, positive definite
function defined as

VL (z, t) ^ -e + 2r r (2-28)
2 2
where e (t) and r (t) are defined in (2-8) and (2-9), respectively. After taking the time
derivative of (2-28) and utilizing (2-9), (2-20), and (2-22), VL (z, t) can be expressed as


VL (z, t)= -yeTe + rTN + rTCBu rTA (k, + Ipxp) r rTA (k, + Ipxp) r (2-29)

r A IIull| I. -1.. (r) rTA IIUII -1,1. (r) rT Aksgn (r)

rTAk sgn (r) + rTNd.

By utilizing the bounding arguments in (2-17) and Assumptions 3 and 5, the upper bound

of the expression in (2-29) can be explicitly determined. Specifically, based on (2-7) of
Assumption 3, the term rTCBu in (2-29) can be upper bounded as

rTCBu < Bd Ir IrJ| u I| (2-30)

After utilizing inequality (2-23) of Assumption 5, the following inequalities can be

developed:

-rTA (k, + Ipp) r rTA (k, + Ipxp<) r < (Amin (ks) + 1) |Ir 2

-rTA u (t)| 1. -.. (r) rTA 1u (t) 1. -.,. (r) < -Emin (k) r ll \u (2-31)

-rTAksgn (r) rTAkysgn (r) < -Amiin (k,7) Ir .









After using the inequalities in (2-30) and (2-31), the expression in (2-29) can be upper
bounded as

VL (, t) < -7 11e 2 + rT? + d Ij E (A (ks) + 1) Ir| 2 (2-32)

EAmin (ku) \r1\ IJu eAmin (ky) |r1| +r TNd,

where the fact that Irl > |r||l V r E IR was utilized. After utilizing the inequalities in
(2-17) and rearranging the resulting expression, the upper bound for VL (z, t) can be
expressed as

S(z, t) < eI|2 _- 2 _- EAmin (ks) IrI 2 +PO Ijrl I : (2-33)

[eAmin (ku) (Bd] Ir \ I|Ju [eAmin (ky) (Nd] Ir

If k, and ky satisfy the sufficient gain conditions in (2-27), the bracketed terms in (2-33)
are positive, and VL (z, t) can be upper bounded using the squares of the components of

z (t) as:
(L (zt)< -7 |e 12 Er12 [min (ks) |r |2 -_ P |r| | I |] (2-34)

By completing the squares, the upper bound in (2-34) can be expressed in a more
convenient form. To this end, the term 4Ei ( s) is added and subtracted to the right hand
4sAmn(kfs)
side of (2-34) yielding
2 [o 2 20
VL(z t) < -'7 |11e |2 2 E Amin (ks) [|| r || nin )l 4 Am inks (2-35)
[_ 2EAnin (k,)JI 4EAin (k,)

Since the square of the bracketed term in (2-35) is always positive, the upper bound can
be expressed as

VL (z, t) < -zTdiag {7pp, Elpxp} + kP (2-36)
da4 Amin (ks)'
where z (t) is defined in (2-13). Hence, (2-36) can be used to rewrite the upper bound of

L (z, t) as
VL ( t) < -min {7, E} -) (2-37)
4 Amin (ks)









where the fact that zTdiag {Ylpxp, Elpxp} z > min {1 e I '. I- was utilized. Provided

the gain condition in (2-27) is satisfied, (2-28) and (2-37) can be used to show that

VL (t) E L,; hence e (t) r (t) E L,. Given that e (t) ,r (t) E Lo, standard linear analysis
methods can be used to prove that e (t) E L, from (2-9). Since e (t) e (t) e Lo, the

assumption that the reference model outputs y, (t) ? (t) E Lo can be used along with

(2-8) to prove that y (t) y (t) E Lo. Given that y (t) y (t) ,e (t) ,r (t) E Lo, the vector

x (t) E Lo, the time derivative (t) E Lo, and (2-10)-(2-12) can be used to show that

x (t) (t) E Lo. Given that x (t) (t) E Lo, Assumptions 1, 2, and 3 can be utilized
along with (2-1) to show that u (t) E Lo.

The definition for VL (z, t) in (2-28) can be used along with inequality (2-37) to show

that VL (z, t) can be upper bounded as

VL(z,t)< -AXVL (z,t) (2-38)

provided the sufficient condition in (2-27) is satisfied. The differential inequality in (2-38)
can be solved as

VL (z, t) < VL (z (0) ,0) exp (-A t) (2-39)

Hence, (2-13), (2-28), and (2-39) can be used to conclude that


||e (t) 11 < | (0) | exp t) t E [0, oo). (2-40)


2.5 Conclusions

A continuous exponentially stable controller was developed for LPV systems with an

unknown state matrix, an uncertain input matrix, and an unknown additive disturbance.

This work presents a new approach to LPV control by inverting the uncertain input

dynamics and robustly compensating for other unknowns and disturbances. The controller

is valid for LPV systems where there are at least as many control inputs as there are

outputs. Using this technique it is possible control LPV systems where there is a high

amount of uncertainty and nonlinearities that invalidate traditional LPV approaches.









Robust dynamic inversion control is possible for a wide range of practical systems that are

approximated as an LPV system with additive disturbances. Future work will focus on

relaxing the assumptions while maintaining the stability and performance.









CHAPTER 3
HYPERSONIC VEHICLE DYNAMICS AND TEMPERATURE MODEL

3.1 Introduction

In this chapter the dynamics of the hypersonic vehicle (HSV) are introduced, in-

cluding both the standard flight dynamics and the structural vibration dynamics. After

the dynamics are developed and the flight and structural components are explained, a

temperature model is introduced. Because changes in temperature change the structural

dynamics, coupled forcing terms change the the flight dynamics. Examples of linear tem-

perature profiles are provided, and some examples of the structural modes and frequencies

are explained.

3.2 Rigid Body and Elastic Dynamics

To incorporate structural dynamics and aerothermoelastic effects in the HSV dynamic

model, an assumed modes model is considered for the longitudinal dynamics [53] as

=Tco (a)-- D gsin (6 a) (3-1)

h = Vsin (0 a) (32)
L +T sin (a) g
a = + Q + cos (0 a) (3-3)
= mV V
= Q (3-4)
M
Q = (3-5)
Iyy
ri = -2... 'i + N, i = 1, 2, 3. (3-6)


In (3-1)-(3-6), V (t) R denotes the forward velocity, h (t) E R denotes the altitude,

a (t) E R denotes the angle of attack, 0 (t) R denotes the pitch angle, Q (t) E R is pitch

rate, and ri (t) E R Vi = 1, 2, 3 denotes the ith generalized structural mode displacement.

Also in (3-1)-(3-6), m R denotes the vehicle mass, lyy E R is the moment of inertia,

g e R is the acceleration due to gravity, (i (t) wi (t) E R are the damping factor and

natural frequency of the ith flexible mode, respectively, T (x) E R denotes the thrust,









D (x) E R denotes the drag, L (x) E R is the lift, M (x) E R is the pitching moment about
the body y-axis, and Ni (x) E R Vi = 1, 2, 3 denotes the generalized elastic forces, where

x (t) E R11 is composed of the 5 flight and 6 structural dynamic states as
[ T
x = V a Q h 0 rj9 r1 2 r2 r93 3j (3-7)

The equations that define the aerodynamic and generalized moments and forces are
highly coupled and are provided explicitly in previous work [10]. Specifically, the rigid
body and elastic modes are coupled in the sense that T (x), D (x), L (x), are functions
of qi (t) and that Ni (x) is a function of the other states. As the temperature profile
changes, the modulus of elasticity of the vehicle changes and the damping factors and
natural frequencies of the flexible modes will change. The subsequent development exploits
an implicit learning control structure, designed based on an LPV approximation of the
dynamics in (3-1)-(3-6), to yield exponential tracking despite the uncertainty due to the
unknown aerothermoelastic effects and additional unmodeled dynamics.
3.3 Temperature Profile Model

Temperature variations impact the HSV flight dynamics through changes in the
structural dynamics which affect the mode shapes and natural frequencies of the vehicle.
The temperature model used assumes a free-free beam [10], which may not capture the
actual aircraft dynamics properly. In reality, the internal structure will be made of a
complex network of structural elements that will expand at different rates causing thermal
stresses. Thermal stresses affect different modes in different manners, where it raises
the frequencies of some modes and lowers others (compared to a uniform degradation
with Young's modulus only). Therefore, the current model only offers an approximate
approach. The natural frequencies of a continuous beam are a function of the mass
distribution of the beam and the stiffness. In turn, the stiffness is a function of Young's
Modulus (E) and admissible mode functions. Hence, by modeling Young's Modulus as a
function of temperature, the effect of temperature on flight dynamics can be captured.











Thermostructural dynamics are calculated under the material assumption that titanium

is below the thermal protection system [9, 12]. Young's Modulus (E) and the natural

dynamic frequencies for the first three modes of a titanium free-free beam are depicted in

Figure 3-1 and Figure 3-2 respectively.


165
16-
155
| 15-
o 145
W 14

S135
0
H 13
125
12 -
115
0 100 200 300 400 500 600 700 800 900
Temperature (F)


Figure 3-1: Modulus of elasticity for the first three dynamic modes of vibration for a free-
free beam of titanium.


In Figure 3-1, the moduli for the three modes are nearly identical. The temperature

range shown corresponds to the temperature range that will be used in the simulation

section. Frequencies in Figure 3-2 correspond to a solid titanium beam, which will not

correspond to the actual natural frequencies of the aircraft. The data shown in Figure 3-1

and Figure 3-2 are both from previous experimental work [47]. Using this data, different

temperature gradients along the fuselage are introduced into the model and affect the

structural properties of the HSV. The simulations in Chapter 4 and Chapter 5 use linearly

decreasing gradients from the nose to the tail section. It's expected that the nose will

be the hottest part of the structure due to aerodynamic heating behind the bow shock

wave. Thermostructural dynamics are calculated under the assumption that there are nine

constant-temperature sections in the aircraft [6] as shown in Figure 3-3. Since the aircraft

is 100 feet long, the length of each of the nine sections is approximately 11.1 feet.



















1st Dynamic Mode
N 55

50-

LL 45
0 100 200 300 400 500 600 700 800 900
2nd Dynamic Mode
NT 160

140-

L 120-
0 100 200 300 400 500 600 700 800 900
3rd Dynamic Mode
NT 300

S250

LL 200
0 100 200 300 400 500 600 700 800 900
Temperature (F)



Figure 3-2: Frequencies of vibration for the first three dynamic modes of a free-free tita-

nium beam.




































Figure 3-3: Nine constant temperature sections of the HSV used for temperature profile

modeling.









Table 3-1: Natural frequencies for 5 linear temperature profiles (Nose/Tail) in degrees
F. Percent difference is the difference between the maximum and minimum frequencies
divided by the minimum frequency.

Mode 900/500 800/400 700/300 600/200 500/100 % Difference
1 (Hz) 23.0 23.5 23.9 24.3 24.7 7.39 %
2 (Hz) 49.9 50.9 51.8 52.6 53.5 7.21 %
3 (Hz) 98.9 101.0 102.7 104.4 106.2 7.38 %


The structural modes and frequencies are calculated using an assumed modes method

with finite element discretization, including vehicle mass distribution and inertia effects.

The result of this method is the generalized mode shapes and mode frequencies for the

HSV. Because the beam is non-uniform in temperature, the modulus of elasticity is also

non-uniform, which produces asymmetric mode shapes. An example of the asymmetric

mode shapes is shown in Figure 3-5 and the asymmetry is due to variations in E resulting

from the fact that each of the nine fuselage sections (see Figure 3-3) has a different

temperature and hence different flexible dynamic properties. An example of some of

the mode frequencies are provided in Table 1, which shows the variation in the natural

frequencies for five decreasing linear temperature profiles shown in Figure 3-4. For all

three natural modes, Table 3-1 shows that the natural frequency for the first temperature

profile is almost 7% lower than that of the fifth temperature profile.

The temperature profile in a HSV is a complex function of the state history, struc-

tural properties, thermal protection system, etc. For the simulations in Chapter 4 and

Chapter 5, the temperature profile is assumed to be a linear function that decreases from

the nose to the tail of the aircraft. The linear profiles are then varied to span a prese-

lected design space. Rather than attempting to model a physical temperature gradient for

some vehicle design, the temperature profile in the simulations in Chapter 4 and Chap-

ter 5 is intended to provide an aggressive temperature dependent profile to examine the

robustness of the controller to such fluctuations.




































2 3 4 5 6
Fuselage section


7 8 9


Figure 3-4: Linear temperature profiles used to calculate values shown in Table 3-1.


a) *.
S0

a -0.1

-0.2
-- 1st
-0.3 -2nd
....... 3rd


20 40 60
Fuselage Position (ft)


80 100


Figure 3-5: Asymetric mode shapes for the hypersonic vehicle. The percent difference was
calculated based on the maximum minus the minimum structural frequencies divided by
the minimum.


800

700

600

g 500

a 400

300

200









3.4 Conclusion

This chapter explains the overall flight and structural dynamics for a HSV, in the

presence of different temperature profiles. These dynamics are important to understand

because changes in the temperature profile modify the dynamics, hence can be modeled

as additive parameter disturbances. In the following chapters, the HSV dynamics will be

reduced to a LPV system with an additive disturbance, and the controller from Chapter

2 will be applied. The temperature profiles will act as the parameter variations. This

chapter was meant to briefly introduce the overall system and explain the structural

modes, shapes, and frequencies. Data was shown to motivate the fact that changes in

temperature substantially affect the overall dynamics.









CHAPTER 4
LYAPUNOV-BASED EXPONENTIAL TRACKING CONTROL OF A HYPERSONIC
AIRCRAFT WITH AEROTHERMOELASTIC EFFECTS

4.1 Introduction

The design of guidance and control systems for airbreathing hypersonic vehicles

(HSV) is challenging because the dynamics of the HSV are complex and highly coupled

[10], and temperature-induced stiffness variations impact the structural dynamics [21].

The structural dynamics, in turn, affect the aerodynamic properties. Vibration in the

forward fuselage changes the apparent turn angle of the flow, which results in changes

in the pressure distribution over the forebody of the aircraft. The resulting changes in

the pressure distribution over the aircraft manifest themselves as thrust, lift, drag, and

pitching moment perturbations [10]. To develop control laws for the longitudinal dynamics

of a HSV capable of compensating for these structural and aerothermoelastic effects,

structural temperature variations and structural dynamics must be considered.

Aerothermoelasticity is the response of elastic structures to aerodynamic heating and

loading. Aerothermoelastic effects cannot be ignored in hypersonic flight, because such

effects can destabilize the HSV system [21]. A loss of stiffness induced by aerodynamic

heating has been shown to potentially induce dynamic instability in supersonic/hypersonic

flight speed regimes [1]. Yet active control can be used to expand the flutter boundary

and convert unstable limit cycle oscillations (LCO) to stable LCO [1]. An active structural

controller was developed [26], which accounts for variations in the HSV structural proper-

ties resulting from aerothermoelastic effects. The control design [26] models the structural

dynamics using a LPV framework, and states the benefits to using the LPV framework

are two-fold: the dynamics can be represented as a single model, and controllers can be

designed that have affine dependency on the operating parameters.

Previous publications have examined the challenges associated with the control of

HSVs. For example, HSV flight controllers are designed using genetic algorithms to search

a design parameter space where the nonlinear longitudinal equations of motion contain









uncertain parameters [4, 30, 49]. Some of these designs utilize Monte Carlo simulations

to estimate system robustness at each search iteration. Another approach [4] is to use

fuzzy logic to control the attitude of the HSV about a single low end flight condition.

While such approaches [4, 30, 49] generate stabilizing controllers, the procedures are

computationally demanding and require multiple evaluation simulations of the objective

function and have large convergent times. An adaptive gain-scheduled controller [55] was

designed using estimates of the scheduled parameters, and a semi-optimal controller is

developed to adaptively attain Ho control performance. This controller yields uniformly

bounded stability due to the effects of approximation errors and algorithmic errors in

the neural networks. Feedback linearization techniques have been applied to a control-

oriented HSV model to design a nonlinear controller [32]. The model [32] is based on

a previously developed [8] HSV longitudinal dynamic model. The control design [32]

neglects variations in thrust lift parameters, altitude, and dynamic pressure. Linear output

feedback tracking control methods have been developed [44], where sensor placement

strategies can be used to increase observability, or reconstruct full state information

for a state-feedback controller. A robust output feedback technique is also developed

for the linear parameterizable HSV model, which does not rely on state observation. A

robust setpoint regulation controller [17] is designed to yield asymptotic regulation in the

presence of parametric and structural uncertainty in a linear parameterizable HSV system.

An adaptive controller [19] was designed to handle (linear in the parameters) mod-

eling uncertainties, actuator failures, and non-minimum phase dynamics [17] for a HSV

with elevator and fuel ratio inputs. Another adaptive approach [41] was recently devel-

oped with the addition of a guidance law that maintains the fuel ratio within its choking

limits. While adaptive control and guidance control strategies for a HSV are investigated

[17, 19, 41], neither addresses the case where dynamics include unknown and unmodeled

disturbances. There remains a need for a continuous controller, which is capable of achiev-

ing exponential tracking for a HSV dynamic model containing aerothermoelastic effects









and unmodeled disturbances (i.e., nonvanishing disturbances that do not satisfy the linear

in the parameters assumption).

In the context of the aforementioned literature, the contribution of the current ef-

fort (and the preliminary effort by the authors [52]) is the development of a controller

that achieves exponential model reference output tracking despite an uncertain model of

the HSV that includes nonvanishing exogenous disturbances. A nonlinear temperature-

dependent parameter-varying state-space representation is used to capture the aerother-

moelastic effects and unmodeled uncertainties in a HSV. This model includes an unknown

parameter-varying state matrix, an uncertain parameter-varying non-square (column

deficient) input matrix, and a nonlinear additive bounded disturbance. To achieve an

exponential tracking result in light of these disturbances, a robust, continuous Lyapunov-

based controller is developed that includes a novel implicit learning characteristic that

compensates for the nonvanishing exogenous disturbance. That is, the use of the implicit

learning method enables the first exponential tracking result by a continuous controller in

the presence of the bounded nonvanishing exogenous disturbance. To illustrate the perfor-

mance of the developed controller during velocity, angle of attack, and pitch rate tracking,

simulations for the full nonlinear model [10] are provided that include aerothermoelastic

model uncertainties and nonlinear exogenous disturbances whose magnitude is based on

airspeed fluctuations.

4.2 HSV Model

The dynamic model used for the subsequent control design is based on a reduction

of the dynamics in (3-1)-(3-6) to the following combination of linear-parameter-varying

(LPV) state matrices and additive disturbances arising from unmodeled effects as


'= A(p(t)) + B (p(t))u + f (t) (4-1)

y = Cx. (42)









In (4-1) and (4-2), x (t) E R1 is the state vector, A (p (t)) E R11x11 denotes a linear

parameter varying state matrix, B (p (t)) E R11x3 denotes a linear parameter varying input

matrix, C E R3x11 denotes a known output matrix, u(t) E IR3 denotes a vector of 3 control

inputs, p (t) represents the unknown time-dependent parameters, f(t) E IR11 represents a

time-dependent unknown, nonlinear disturbance, and y (t) E IR3 represents the measured

output vector of size 3.

4.3 Control Objective

The control objective is to ensure that the output y(t) tracks the time-varying output

generated from the reference model like stated in Chapter 2. To quantify the control

objective, an output tracking error, denoted by e (t) E R3, is defined as


e y ym = C (x m) (4-3)

To facilitate the subsequent analysis, a filtered tracking error denoted by r (t) E IR3, is

defined as

r = + ye (4-4)

where y R3 is a positive definite diagonal, constant control gain matrix, and is selected

to place a relative weight on the error state verses its derivative. Based on the control

design presented in Chapter 2 the control input is designed as

S= -kr (CBo)-1 [(k, + I33) e t) (k, + I33) e (0) + v (t)] (4-5)

where v (t) E IR3 is an implicit learning law with an update rule given by


iv (t) = k | u (t) l sgn (r (t)) + (k, + I3x3) ye (t) + k-sgn (r (t)) (4-6)

and kr, ku, ks, k E IR3x3 denote positive definite, diagonal constant control gain matrices,

Bo E R11x3 represents a known nominal input matrix, sgn (.) denotes the standard

signum function where the function is applied to each element of the vector argument,

and 13x3 denotes a 3 x 3 identity matrix. To illustrate the performance of the controller








and practicality of the assumptions, a numerical simulation was performed on the full
nonlinear longitudinal equations of motion [10] given in (3-1)-(3-6). The control inputs
were selected as u = [e (t) Oc (t) i (t as in previous research [41], where 6e (t)
and c6 (t) denote the elevator and canard deflection angles, respectively, Of (t) is the fuel
equivalence ratio. The diffuser area ratio is left at its operational trim condition without
loss of generality (Ad (t) = 1). The reference outputs were selected as maneuver oriented
outputs of velocity, angle of attack, and pitch rate as y = V (t) a (t) Q (t) where
the output and state variables are introduced in (3-1)-(3-5). In addition, the proposed
controller could be used to control other output states such as altitude provided the
following condition is valid. The auxiliary matrix 2 (p (t)) E Rqxq is defined as

Sa CBkr (CBo)-l (47)

where 0 (p (t)) can be separated into diagonal (i.e., A (p (t)) E IRqx) and off-diagonal (i.e.,

A (p (t)) E RqIx) components as
0 = A + A. (48)

The uncertain matrix 2 (p (t)) is diagonally dominant in the sense that

Amin (A)- I||A||o > e (4-9)

where e R I+ is a known constant. While this assumption cannot be validated for a
generic HSV, the condition can be checked (within some certainty tolerances) for a given
aircraft. Essentially, this condition indicates that the nominal value Bo must remain
within some bounded region of B. In practice, bands on the variation of B should be
known, for a particular aircraft under a set of operating conditions, and this band could
be used to check the sufficient conditions. For the specific HSV example this Chapter
simulates, the assumtion in 4-9 is valid.









4.4 Simulation Results

The HSV parameters used in the simulation are m = 75, 000 lbs Iyy = 86723

lbs ft2, and g = 32.174 ft/s2.as defined in (3-1)-(3-6). The simulation was executed for

35 seconds to sufficiently cycle through the different temperature profiles. Other vehicle

parameters in the simulation are functions of the temperature profile. Linear temperature

profiles between the forebody (i.e., Tfb e [450, 900]) and aftbody (i.e., Tab e [100, 800])

were used to generate elastic mode shapes and frequencies by varying the linear gradients

as

{( T t) 450 + 350 cos (ft) if Tfb (t) > Tab (t)
Tfb (t) = 675 + 225 cos -t Tab (t) =
(10/ Tfb (t) otherwise.

(4-10)

Figure 4-1 shows the temperature variation as a function of time. The irregularities seen

in the aftbody temperatures occur because the temperature profiles were adjusted to

ensure the tail of the aircraft was equal or cooler than the nose of the aircraft according

to bow shockwave thermodynamics. While the shockwave thermodynamics motivated

the need to only consider the case when the tail of the aircraft was equal or cooler than

the nose of the aircraft, the shape of the temperature profile is not physically motivated.

Specifically, the frequencies of oscillation in (4-10) were selected to aggressively span the

available temperature ranges. These temperature profiles are not motivated by physical

temperature gradients, but motivated by the desire to generate a temperature disturbance

to illustrate the controller robustness to the temperature gradients. The simulation

assumes the damping coefficient remains constant for the structural modes ((i = 0.02).

In addition to thermoelasticity, a bounded nonlinear disturbance was added to the

dynamics as

f= [fv fa fQ 0 0 0 fii 0 fi2 0 fi3 (4-11)










1000
800
1 600
S400-

z 200
0
0 5 10 15 20 25 30 35
Time (s)

800-IIIIII

S600-

400

200

0 5 10 15 20 25 30 35
Time (s)

Figure 4-1: Temperature variation for the forebody and aftbody of the hypersonic vehicle
as a function of time.


where f (t) E IR denotes a longitudinal acceleration disturbance, f (t) IR denotes a angle

of attack rate of change disturbance, fQ(t) E IR denotes an angular acceleration distur-

bance, and fi (t), fi2 (t), fi3(t), E IR denote structural mode acceleration disturbances. The

disturbances in (4-11) were generated as an arbitrary exogenous input (i.e., unmodeled

nonvanishing disturbance that does not satisfy the linear in the parameters assumption)

as depicted in Figure 4-2. However, the magnitudes of the disturbances were motivated by

the scenario of a 300 ft/s change in airspeed. The disturbances are not designed to mimic

the exact effects of a wind gust, but to demonstrate the proposed controller's robustness

with respect to realistically scaled disturbances. Specifically, a relative force disturbance is

determined by comparing the drag force D at Mach 8 at 85, 000 ft (i.e., 7355 ft/s) with

the drag force after adding a 300 ft/s (e.g., a wind gust) disturbance. Using Newton's

second law and dividing the drag force differential AD by the mass of the HSV m, a

realistic upper bound for an acceleration disturbance fy (t) was determined. Similarly, the

same procedure can be performed, to compare the change in pitching moment AM caused

by a 300 ft/s head wind gust. By dividing the moment differential by the moment of











x 10
-1

0 5 10 15 20 25 30 35
10
D 0
S? 10----cl----------- n----r---
S10
0 5 10 15 20 25 30 35
~ 2
0
-2
0 5 10 15 20 25 30 35
005
0
-005

-0 01 I----- ---------------------------------------------------
0 5 10 15 20 25 30 35
001
0
-001
0 5 10 15 20 25 30 35
x 10


0 5 10 15 20 25 30 35
Time (s)


Figure 4-2: In this figure, fi denotes the ith element in the disturbance vecor f. Distur-
bances from top to bottom: velocity fy, angle of attack fd, pitch rate fQ, the 1st elastic
structural mode i1, the 2nd elastic structural mode i2, and the 3rd elastic structural mode
173, as described in (4-11).


inertia of the HSV Iyy, a realistic upper bound for fQ (t) can be determined. To calculate

a reasonable angle of attack disturbance magnitude, a vertical wind gust of 300 ft/s is

considered. By taking the inverse tangent of the vertical wind gust divided by the forward

velocity at Mach 8 and 85,000 ft, an upper bound for the angle of attack disturbance

fa(t) can be determined. Disturbances for the structural modes fji(t) were placed on the

acceleration terms with i((t), where each subsequent mode is reduced by a factor of 10

relative to the first mode, see Figure 4-2.

The proposed controller is designed to follow the outputs of a well behaved reference

model. To obtain these outputs, a reference model that exhibited favorable characteristics

was designed from a static linearized dynamics model of the full nonlinear dynamics

[10]. The reference model outputs are shown in Figure 4-3. The velocity reference output

follows a 1000 ft/s smooth step input, while the pitch rate performs several 1 /s

maneuvers. The angle of attack stays within 2 degrees.










8500

O 8000

> 7500-

7000
0


2


E 0
ac


5 10 15 20 25 30 35






5 10 15 20 25 30 35






5 10 15 20 25 30 35
Time (s)


Figure 4-3: Reference model ouputs yi, which are the desired trajectories for top: velocity
Vm (t), middle: angle of attack a, (t), and bottom: pitch rate Qm (t).


The control gains for (4-3)-(4-4) and (4-5)-(4-6) are selected as


y= diag {10, 10} k, = diag {5,1, 300}

k = diag {0.1, 0.01, 0.1}


k, = diag {0.01, 0.001, 0.01}

kr = diag {1, 0.5, 1}.


The control gains in (4-12) were obtained using the same method as in Chapter 5. In

contrast to this suboptimal approach used, the control gains could have been adjusted

using more methodical approaches as described in various survey papers on the topic

[24, 46].

The C matrix and knowledge of some nominal Bo matrix must be known. The C

matrix is given by:


1 0 0 0 0 0 0 0 0 0 0
o1000000000

C= 0 1 0 0 0 0 0 0 0 0 0


(4-12)


(4-13)











8400
8200
8000
S7800-
> 7600
7400
7200
0 5 10 15 20 25 30 35


02



S-0 4 -
LU
-0 6
-08-
-1

0 5 10 15 20 25 30 35
Time (s)


Figure 4-4: Top: velocity V (t), bottom: velocity tracking error ev (t).



for the output vector of (4-2), and the Bo matrix is selected as

T
-32.69 -0.017 -9.07 0 0 0 2367 0 -1132 0 -316

Bo = 25.72 -0.0111 9.39 0 0 0 3189 0 2519 0 2067 (4-14)


42.84 -0.0016 0.0527 0 0 0 42 13 0 92.12 0 -80.0


based on a linearized plant model about some nominal conditions.

The HSV has an initial velocity of Mach 7.5 at an altitude of 85, 000 ft. The velocity,

and velocity tracking errors are shown in Figure 4-4. The angle of attack and angle of

attack tracking error is shown in Figure 4-5. The pitch rate and pitch tracking error

is shown in Figure 4-6. The control effort required to achieve these results is shown in

Figure 4-7. In addition to the output states, other states such as altitude and pitch angle

are shown in Figure 4-8. The structural modes are shown in Figure 4-9.

4.5 Conclusion

This result represents the first ever application of a continuous, robust model refer-

ence control strategy for a hypersonic vehicle system with additive bounded disturbances






























5 10 15 20 25 30 35


0 5 10 15 20 25 30 35
Time (s)


Figure 4-5: Top: angle of attack a (t), bottom: angle of attack tracking error e (t).















0i
15


05-


10 15 20 25 30 35


D 005
0


S- 01
f
n a r


GC -"- --"'" ~


Figure 4-6: Top: pitch rate Q (t), bottom: pitch rate tracking error eQ (t).


0 5 10 15 20 25 30 35
Time (s)

















15




u_






2o



15

10 10 15 20 25 30 35
o0 5 10 15 20 25 30 35


10

0-

10
0 5 10 15
Time (s)


20 25 30 35


fuel equivalence ratio of. Middle: elevator deflection 6,. Bottom: Canard


x 10
85

84

S83

82-

81


10 15 20 25 30 35


10 15 20 25 30 35
Time (s)


Figure 4-8: Top: altitude h (t), bottom: pitch angle 0 (t).


Figure 4-7: Top:

deflection cc.















5 10 15 20 25 30 35


S10
5

10
0 5 10 15 20 25 30 35





-5i -
0 5 10 15 20 25 30 35
Time (s)

Figure 4-9: Top: 1st structural elastic mode rll. Middle: 2nd structural elastic mode l2-.
Bottom: 3'd structural elastic mode r3-.


and aerothermoelastic effects, where the control input is multiplied by an uncertain, col-

umn deficient, parameter-varying matrix. A potential drawback of the result is that the

control structure requires that the product of the output matrix with the nominal control

matrix be invertible. For the output matrix and nominal matrix, the elevator and canard

deflection angles and the fuel equivalence ratio can be used for tracking outputs such as

the velocity, angle of attack, and pitch rate or velocity and the flight path angle, or veloc-

ity, flight path angle and pitch rate. Yet, these controls can not be applied to solve the

altitude tracking problem because the altitude is not directly controllable and the product

of the output matrix with the nominal control matrix is singular. However, the integrator

backstepping approach that has been examined in other recent results for the hypersonic

vehicle could potentially be incorporated in the control approach to address such objec-

tives. A Lyapunov-based stability analysis is provided to verify the exponential tracking

result. Although the controller was developed using a linear parameter varying model of

the hypersonic vehicle, simulation results for the full nonlinear model with temperature

variations and exogenous disturbances illustrate the boundedness of the controller with









favorable transient and steady state tracking errors. These results indicate that the LPV

model with exogenous disturbances is a reasonable approximation of the dynamics for the

control development.









CHAPTER 5
CONTROL PERFORMANCE VARIATION DUE TO NONLINEAR
AEROTHERMOELASTICITY IN A HYPERSONIC VEHICLE: INSIGHTS FOR
STRUCTURAL DESIGN

5.1 Introduction

Typically, controllers are developed to achieve some performance metrics for a given

HSV model. However, improved performance and robustness to thermal gradients could

result if the structural design and control design were optimized in unison. Along this

line of reasoning in [16, 23], the advantage of correctly placing the sensors is discussed,

representing a move towards implementing a control friendly design. A previous control

oriented design analysis in [6] states that simultaneously optimizing both the structural

dynamics and control is an intractable problem, but that control-oriented design may be

performed by considering the closed-loop performance of an optimal controller on a series

of different open-loop design models. The best performing design model is then said to

have the optimal dynamics in the sense of controllability.

Knowledge of the better performing thermal gradients can provide design engineers

insight to properly weight the HSV's thermal protection system for both steady-state and

transient flight. The preliminary work in [6] provides a control-oriented design architecture

by investigating control performance variations due to thermal gradients using an too con-

troller. Chapter 5 seeks to extend the control oriented design concept to examine control

performance variations for HSV models that include nonlinear aerothermoelastic distur-

bances. Given these disturbances, Chapter 5 focuses on examining control performance

variations for our previous model reference robust controller in [52] and previous chapters

to achieve a nonlinear control-oriented analysis with respect to thermal gradients. By

analyzing the control error and input norms over a wide range of temperature profiles an

optimal temperature profile range is suggested. Based on preliminary work done in [50], a

number of linear temperature profile models are examined for insight into the structural

design. Specifically, the full set of nonlinear flight dynamics will be used and control effort,










errors, and transients such as steady-state time and peak to peak error will be examined

across the design space.

5.2 Dynamics and Controller

The HSV dynamics used in this chapter are the same is in Chapter 3 and equations

(3-1)-(3-6). Similarly as in the results in Chapter 4, the dynamics in (3-1)-(3-6) are

reduced to the linear parameter model used in (2-1) and (2-2) with p = q = 2. For the

control-oriented design analysis, a number of different linear profiles are chosen [6, 50]

with varying nose and tail temperatures as illustrated in Figure 5-1. This set of profiles

define the space from which the control-oriented analysis will be performed. As seen in

Figure 5-1, the temperature profiles are linear and decreasing towards the tail. These

profiles are realistic based on shock formation at the front of the vehicle and that the

temperatures are within the expected range for hypersonic flight. Based on previous


IIII I




21 II I -


41111
1 2 46






1 2 3 4 5 6
Fuselage Station

Figure 5-1: HSV surface temperature profiles. Tnose
[100F, 800F].


7 8 9


[450F, 900F], and Ttil


control development in [52] and in the previous Chapters, the control input is designed as


S= -kr (CBo)-1 [(ks + I33) e (t) (k + I33) e (0) + v (t)]


(5-1)









where v (t) E IR2 is an implicit learning law with an update rule given by


S (t) = k, I|u (a) || sgn (r (a)) + (k, + 13x3) ye (a) + kysgn (r (a)) (5-2)

where kr, k,, k, k, E R 2x2 denote positive definite, diagonal constant control gain

matrices, Bo0 R112 represents a known nominal input matrix, sgn (-) denotes the

standard signum function where the function is applied to each element of the vector

argument, and I2x2 denotes a 2 x 2 identity matrix.

5.3 Optimization via Random Search and Evolving Algorithms

For each of the individual temperature profiles examined, the control gains kr,

k,, k,, k~, and 7 in (5-1)-(5-2) were optimized for the specific plant model using a

combination of random search and evolving algorithms. Since both the plant model

simulation dynamics and the control scheme itself are nonlinear, traditional methods for

linear gain tuning optimization could not be used. The selected method is a combination

of a control gain random search space, combined with an evolving algorithm scheme

which allows the search to find a nearest set of optimal control gains for each individual

plant. This method allows one near-optimal controller/plant to be compared to the other

near-optimal controller/plants and provides a more accurate way of comparing cases.

The first step in the control gain optimization search is a random initialization. For

this numerical study, 1000 randomly selected sets of control gains are used for a given

plant model. A 1000 initial random set was chosen to provide sufficient sampling to

insure global convergence. The following section has a specific example case for one of

the temperature profiles. After the 1000 control gain sets are selected, all the sets are

simulated on the given plant model and the controller in (5-1) and (5-2) is applied to

track a certain trajectory as well as reject disturbances. The trajectory and disturbances

were chosen the same throughout the entire study so that the only variations will be due

to the plant model and control gains. The example case section explicitly shows both the

desired trajectory and the disturbances injected.









After the 1000 initial random control gain search is performed, the top five perform-

ing sets of control gains are chosen as the seeds for the evolving algorithm process. This

process is repeated for four generations, each with the best five performing sets of control

gains at each step. All evolving algorithms have some or all of the following characteris-

tics: elitism, crossover, and random mutation. This particular numerical study uses all

three as follows. The best five performing sets in each subsequent generation, are chosen

as elite and move onto the next iteration step. From those five, each set of control gains

is averaged with all other permutations of control gains in the elite set. For instance, if

parent #1 is averaged with #2 to form an offspring set of control gains. Parent #1 is

also averaged with parent #3 for a separate set of offspring control gains. In this way, all

combinations of crossover are performed. The permutations of the five elite parents yield a

total of 10 offspring.

The next generation contains the five elite parents from the generation before,

as well as the 10 crossover offspring, for a total of 15. Each of these 15 sets of control

gains is then mutated by a certain percentage. Based on preliminary numerical studies

performed on this specific example, the random mutations were chosen to be 20% for the

first two generations and 5% for the final two generations. This produced both global

search in the beginning, and refinement at the end of the optimization procedure. The

set of 15 remains, with the addition of 20 mutated sets for each of the 15. This gives a

total control gain set for the next generation of search of 315. As stated, there are four

evolving generations after the first 1000 random control sets. The combined number of

simulations with different control gains performed for a single temperature profile case is

2260. These particular numbers were chosen based on preliminary trial optimization cases,

with the goal to provide sufficient search to achieve convergence of a minimum for the cost

function. The following section illustrates the entire procedure for a single temperature

profile case.









The cost function is designed such that the errors and control inputs are the same

order of magnitudes, so that they can more easily be added and interpreted. This is

important because for example, the desired velocity is high (in the thousands of ft/s) and

the desired pitch rate is small (fraction of radians). Explicitly, the cost function is taken as

the sum of the control and error norms and is scaled as


Gerr= 100e 1000 eo (5-3)
2

and

co = j 10 (5-4)
L 2
where ev (t) Q (t) E IR are the velocity and pitch rate errors, respectively, and

6, (t), Of (t) E IR are the elevator and fuel ratio control inputs, respectively, and 2| |

denotes the standard 2-norm. The combined cost function is the sum of the individual

components and can be explicitly written as


Ltot = Qerr + Lcon (5-5)

where Qtot is the cost value associated with all subsequent optimal gain selection.

5.4 Example Case

The HSV parameters used in the simulation are m = 75, 000 lbs Iyy = 86723

lbs ft2, and g = 32.174 ft/s2.as defined in (3-1)-(3-6). To illustrate how the random

search and evolving optimization algorithms work, this section is provided as a detailed

example. First the output tracking signal and disturbances are provided, followed by the

optimization and convergence procedure. The goal of this section is to demonstrate that

the specific number of elites, offspring, mutations, and generations listed in the previous

section are justified in that the cost function shows asymptotic convergence to a minimum.

The desired trajectory is shown in Figure 5-2 and the disturbance is depicted in Figure

5-3, where the magnitudes are chosen based on previous analysis performed in [52]. The

example case is based on a temperature profile with Tnose = 3500F and Trtil = 2000F. For





















6) 0.5

. 0
n-
2 -0.5

-1


2 4 6 8

0 2 4 6 8 1(


7900


7850


7800


Time (s)


Figure 5-2: Desired trajectories: pitch rate Q (top) and velocity V (bottom).


x 10
1-

gE 0

_


0 1 2 3 4 5 6 7 8 9 10



5


5
0 1 2 3 4 5 6 7 8 9 10



0


0 1 2 3 4 5 6 7 8 9 10


1 2 3 4 5
Time (s)


6 7 8 9 10


Figure 5-3: Disturbances for velocity V (top), angle of attack a (second from top), pitch

rate Q (second from bottom) and the 1st structural mode (bottom).


-0


0 05


0
ou-



-005
0


c?














S0.02

) IG


-0.02
0 2 4 6 8 10

0.5

0

S-0.5

-1

-1.5
0 2 4 6 8 10
Time (s)


Figure 5-4: Tracking errors for the pitch rate Q in degrees/sec (top) and the velocity V in
ft/sec (bottom).


this particular case, Figure 5-4 and Figure 5-5 show the tracking errors and control inputs,

respectively, for the control gains


11.17 0 14.55 0 25.99 0
y ks k -
0 39.61 0 224.0 0 0.618


20.7 0 0.915 0
k = kr= (5-6)
0 0.369 0 0.898


The cost functions have values as seen in Figure 5-6. In Figure 5-6 the control input

cost remains approximately the same, but as the control gains evolve, the error cost and

hence total cost decrease asymptotically. The 1st five iterations correspond to the top five

performers in the first 1000 random sample, and each subsequent five correspond to the

top five for the subsequent evolution generations. To limit the optimization search design

space, all simulations are performed with two inputs and two outputs. As indicated in the

cost functions listed in (5-3)-(5-5), the inputs include the elevator deflection 6, (t) and the

fuel ratio 0f (t), and the outputs are the velocity V (t) and the pitch rate Q (t).












































Time (s)


Figure 5-5: Control inputs for the elevator 6, in degrees (top) and the fuel ratio of (bot-

tom).







x 105
18

0 16- -

1 0 4

12
0 5 10 15 20 25


x 10
9596
g 9 5955-
2 9595-

9 5945
9 594
0


x 10
7

6
S5-
U 4-


Iteration #


Figure 5-6: Cost function values for the total cost Qtot

and the error cost err (bottom).


(top), the input cost Qco, (middle)









5.5 Results

The results of this section cover all the temperature profiles shown in Figure 5-1. The

data presented includes the cost functions as well as other steady-state and transient data.

Included in this analysis are the control cost function, the error cost function, the peak-

to-peak transient response, the time to steady-state, and the steady-state peak-to-peak,

for both control and error signals. Because the data contains noise, a smoothed version

of each plot is also provided. The smoothed plots use a standard 2-dimensional filtering,

where each point is averaged with its neighbors. For instance for some variable cv, the

averaged data is generated as

(4'...' ; + i,+lj + i-l,j + .' ;+1 + '-.' -1)57
=- i(57)
8

The averaging formula shown in (5-7) is used for filtering of all subsequent data. Also,

note that the lower right triangle formation is due to the design space only containing

temperature profiles where the nose is hotter than the tail. This is due to the assumption

that because of aerodynamic heating from the extreme speeds of the HSV, that this

will always be the case. These temperature profiles relate to the underlying structural

temperature, not necessarily the skin surface temperature. Figure 5-7 and Figure 5-8 show

the control cost function value ,co,. Note that there is a global minimum, however also

note for all of the control norms the total values are approximately the same. This data

indicates that while other performance metrics varied widely as a function of temperature

profile, the overall input cost remains approximately the same. In Figure 5-9 and Figure

5-10, the error cost is shown. Note that there is variability, but that there seems to

be a region of smaller errors in the cooler section of the design space. Namely, where

Tnose E [200, 600] F and Trail e [100, 250] F. Combining the control cost function with

the error cost function yields the total cost function (and its filtered counterpart) depicted

in Figure 5-11 (and Figure 5-12, respectively). The importance of this plot is that the

total cost function was the criteria for which the control gains were optimized. In this















900
















300


200


100
100 200 300 400 500 600
Tail Temp (F)


700 800 900


Figure 5-7: Control cost function co,, data as a function of tail and nose temperature
profiles.


400 500 600
Tail Temp (F)


x 104
9.5956


9.5954


9 5952


9.595


9.5948


9 .5944


9.5944


9.5942


Figure 5-8: Control cost function co,, data (filtered) as a function of tail and nose tem-
perature profiles.


x104
9.5956

9.5954

9.5952

9.595

9.5948

9.5946

9.5944

9.5942

9.594

9.5938
































200

100
100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-9: Error cost function ,,er data as a function of tail and nose temperature pro-
files.


300 400 500 600
Tail Temp (F)


Figure 5-10: Error cost function 2,er data (filtered) as a function of tail and nose tempera-
ture profiles.










x 105







1.4
E ----



1.35
300

200 1.3

100
100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-11: Total cost function Qt,t data as a function of tail and nose temperature pro-
files.


sense, the total cost plots represent where the temperature parameters are best suited for

control based on the given cost function. Since the cost of the control input is relatively

constant, the total cost largely shows the same pattern as the error cost. In addition to

the region between T,,os E [200, 600] F and Trail e [100, 250] F, there also seems to be

a region between Tose = 9000F and Trail e [600, 900] F, where the performance is also

improved.

The control cost, error cost, and total cost were important in the optimization of

the control gains and were used as the criteria for selecting which gain combination was

considered near optimal. However, there are potentially other performance metrics of

value. In addition to the optimization costs, the peak-to-peak transient errors, time to

steady-state, and steady-state peak-to-peak errors were examined for further investigation.

The peak-to-peak transient error is produced by taking the difference from the maximum

and minimum transient tracking errors. The peak-to-peak error for the pitch rate Q (t)

is plotted in Figure 5-13 and Figure 5-14, and the peak-to-peak for the velocity V (t) is











x 105
900
1.5

800

700

1.45



0
1.4





11,I,,1.35
100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-12: Total cost function Qtot data (filtered) as a function of tail and nose tempera-
ture profiles.


plotted in Figure 5-15 and Figure 5-16. The pitch rate peak-to-peak errors do not have

a large variation for the different plants, other than a noticeable poor performing region

around Tnose = 5500F and Ttail = 4500F. The velocity peak-to-peak has a minimum

around the similar Tnose E [200, 600] F and Trail e [100, 250] F. The velocity peak-to-

peak has minimums when the pitch rate has maximums, indicating a degree of trade off

between better velocity performance, but worse pitch rate performance, and vice versa.



An examination of the time to steady-state plots for pitch rate and velocity shown in

Figures 5-17-5-20 indicates relatively similar transient times, with a few outliers. Having

little variation means that all the plant models are similar in the transient times with

this particular control design. The time to steady-state is calculated by looking at the

transient performance and extracting the time it takes for the error signals to decay below

the steady-state peak-to-peak error value.






















700


" 600

E
. 500

0
Z .n"


100 200 300 400 500 600 700 800 900
Tail Temp (F)



Figure 5-13: Peak-to-peak transient error for the pitch rate Q (t) tracking error in
deg./sec..








900


800 0.4

700
0.35

600

S0.3
500

0
z 400 0.25


300
0.2
200

0.15
100
100 200 300 400 500 600 700 800 900
Tail Temp (F)



Figure 5-14: Peak-to-peak transient error (filtered) for the pitch rate Q (t) tracking error
in deg./sec..





































1UU 2UU 3UU 4UU bUU 6UU 7UU BUU 9UU
Tail Temp (F)


Figure 5-15: Peak-to-peak transient error for the velocity V (t) tracking error in ft/sec..















1.65



1.6



1.55



1.5
I (11C1
100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-16: Peak-to-peak transient error (filtered) for the velocity V (t) tracking error in
ft./sec..


























S600

E
. 500

0
Z Anr


100 200 300 400 500 600
Tail Temp (F)


700 800 900


Figure 5-17: Time to steady-state for the pitch rate Q (t) tracking error in seconds.


200 300 400 500 600
Tail Temp (F)


700 800 900


Figure 5-18: Time to steady-state (filtered) for the pitch rate Q (t) tracking error in sec-

onds.


~ 600

E
4 500

0
Z Anr


100-
100




























11.5


4UU bUU bUU
Tail Temp (F)


Figure 5-19: Time to steady-state for the velocity V (t) tracking error in seconds.


400 500 600
Tail Temp (F)


Figure 5-20: Time to steady-state (filtered) for the velocity V (t) tracking error in seconds.












900 0.022
0.02

0.018
0.016

,.,,.,-0.014

0.012
0.01
0.008
-"-' 0.006

0.004

0.002
100
100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-21: Steady-state peak-to-peak error for the pitch rate Q (t) in deg./sec..


Finally, the steady-state peak-to-peak error values can be examined for both output

signals. The steady-state peak-to-peak errors are calculated by waiting until the error

signal falls to within some non-vanishing steady-state bound after the initial transients

have died down, and then measuring the maximum peak-to-peak error within that

bound. The plots for steady-state peak-to-peak error for the pitch rate and velocity are

shown in Figures 5-21 5-24. The steady-state peak-to-peak errors show a minimum in

the similar region as seen for other performance metrics, i.e. Tnose E [200, 600] F and

Ttai e [100, 250] F.

By normalizing all of the previous data about the minimum of each set of data, and

then adding the plots together, a combined plot is obtained. This plot assumes that the

designer weights each of the plots equally, but the method could be modified if certain

aspects were deemed more important than others. Explicitly, data from each metric was

combined as according to
I1 A ,j (A) (58)
A =l min (i,j (A))

















900


800


700


600


0 1

2








100
100


700 800 900


Figure 5-22: Steady-state peak-to-peak error (filtered) for the pitch rate Q (t) in deg./sec..


ZUU JUU 4UU oUU oUU
Tail Temp (F)


700 800 900


Figure 5-23: Steady-state peak-to-peak error for the velocity V (t) in ft./sec..


200 300 400 500 600
Tail Temp (F)


x 10-3
12



10



8



6



4



2


300





100
100











x 10-3



10


IIII8

i-.-.--
.6
0

4





100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-24: Steady-state peak-to-peak error (filtered) for the velocity V (t) in ft./sec.


where ip is the new combined and normalized temperature profile data, A is the number of

data sets being combined, and i, j are the location coordinates of the temperature data.

Figure 5-25 shows this combination of control cost, error cost, peak-to-peak error, time to

steady-state, and steady-state peak-to-peak error for both pitch rate and velocity tracking

errors. By examining this cost function, an optimal region between Tnose E [200, 600] F

and Trail e [100, 250] F is determined.

In addition, optimal regions for the control gains can be examined. The control gains

used for this problem are shown in (5-1) and (5-2) having the form


7= 0s k, 0 k, O

0 72 0 k,2 O k2




0 kY2 0 kFr


By examining the control gains the maximum, minimum, mean, and standard

deviation can be computed for all sets of control gains found to be near optimal. Table 5-1











SUU
7


6





4
Z 4C0C0






100 200 300 400 500 600 700 800 900
Tail Temp (F)


Figure 5-25: Combined optimization p chart of the control and error costs, transient and
steady-state values.

Table 5-1: Optimization Control Gain Search Statistics
^71 '72 kC1 ks 2 k kU1 kk2 k kY1 k-Y2 kr1 kr2
Mean 25.35 36.60 16.07 265.3 28.38 9.65 27.43 14.12 0.972 0.8958
Std. 7.72 7.64 7.05 85.6 13.1 7.98 13.5 10.6 0.1565 0.133
Max 44.6 55.3 53.6 423.5 57.3 36.4 62.1 39.1 1.318 1.201
Min 7.14 3.58 6.30 9.762 0.360 0.050 0.392 0.110 0.658 0.6640


shows the control gain statistics. This data is useful in describing the optimal range for

which control gains were selected. By knowing the region of near optimal attraction for

the control gains, a future search could be confined to that region. The standard deviation

also says something about the sensitivity of the control/aircraft dynamics, where larger

standard deviations mean that particular gain has less effect on the overall system and

vice a versa.

5.6 Conclusion

A control-oriented analysis of thermal gradients for a hypersonic vehicle (HSV)

is presented. By incorporating nonlinear disturbances into the HSV model, a more

representative control-oriented analysis can be performed. Using the nonlinear controller

developed in Chapter 2 and Chapter 4, performance metrics were calculated for a number









of different HSV temperature profiles based on the design process initially developed

in [6, 50]. Results from this analysis show that there is a range of temperature profiles

that maximizes the controller effectiveness. For this particular study, the range was

Tnose E [200,600] F and Ttail e [100, 250] F. In addition, this research has shown

the range of control gains, useful for future design and numerical studies. This control-

oriented analysis data is useful for HSV structural designs and thermal protection systems.

Knowledge of a desirable temperature profile and control gains will allow engineers

and designers to build a HSV with the proper thermal protection that will keep the

vehicle within a desired operating range based on control performance. In addition, this

numerical study provides information that can be further used in more elaborate analysis

processes and demonstrates one possible method for obtaining performance data for a

given controller on the complete nonlinear HSV model.









CHAPTER 6
CONCLUSIONS AND FUTURE WORK

6.1 Conclusions

A new type on controller is developed for LPV systems that robustly compensates

for the unknown state matrix, disturbances, and compensates for the uncertainty in the

input dynamic inversion. In comparison with previous results, this work presents a novel

approach in control design that stands out from the classical gain scheduling techniques

such as standard scheduling, the use of LMIs, and the more recent development of LFTs,

including their non-convex /-type optimization methods. Classical problems such as gain

scheduling suffer from stability issues and the requirement that parameters only change

slowly, limiting their use to quasi-linear cases. LMIs use convex optimization, but typically

require the use of numerical optimization schemes and are analytically intractable except

in rare cases. LFTs further the control design for LPV systems by using small gain the-

ory, however they cannot deal explicitly with uncertain parameters. To handle uncertain

parameters, the LFT problem is converted into a numerical optimization problem such

as /-type optimization. /-type optimization is non-convex and therefore solutions may

not be found even when they exist. The robust dynamic inversion control developed for

uncertain LPV systems alleviates these problems. As long as some knowledge of the input

matrix is known and certain invertability requirements are met then a stabilizing con-

troller always exists. Proofs provided show that the controller is robust to disturbances,

state dynamics, and uncertain parameters by using a new robust controller technique with

exponential stability.

Common applications for LPV systems are flight controllers. This is because his-

torically flight trajectories vary slowly with time and are well suited to the previously

mentioned LPV control schemes such as gain scheduling. Recent advances in technology

and aircraft design as well as more dynamic and demanding flight profiles have increased

the demand on the controllers. In these demanding dynamic environments, parameters









no longer change slowly and may be unknown or uncertain. This renders previous con-

trol designs limiting. Motivated by this fact and specifically using the dynamics of an

air-breathing HSV, the dynamics are shown to be modeled as an LPV system with un-

certainties and disturbances. This work motivates the design and testing of the robust

dynamic inversion controller on a temperature varying HSV. Using unknown temperature

profiles, while simultaneously tracking an output trajectory, the robust controller is shown

to compensate for unknown time-varying parameters in the presence of disturbances for

the HSV. Using one set of control gains it was shown that stable control was maintained

over the entire design space while performing maneuvers. Even though the control was de-

veloped for LPV systems, the simulation results are performed on the full nonlinear HSV

flight and structural dynamics, hence validated the control-oriented modeling assumptions.

Finally, a numerical optimization scheme was performed on the same HSV model,

using a combination of random search and evolving algorithms to produce dynamic

optimization data for the combined vehicle and controller. Regions of optimality were

shown to provide feedback to design engineers on the best suitable temperature profile

parameter space. To remove ambiguity, the controller for each individual temperature

profile case was optimally tuned and the tracking trajectory and disturbances were kept

the same. Analytical methods do not exist for optimal gain tuning nonlinear controllers on

nonlinear systems Hence, a numerical optimizing scheme was developed. By strategically

searching the control gain space values were obtained, and the performance metrics at

that point were compared across the vehicle design space. This work may be useful for

future design problems for HSVs where the structural and dynamic design are performed

in conjunction with the control design.

6.2 Contributions

A new robust dynamic inversion controller was developed for general perturbed LPV

systems. The control design requires knowledge of a best guess input matrix and at

least as many inputs as tracked outputs. In the presence an unknown state matrix,









parameters, and disturbance, and with an uncertain input matrix, the developed

control design provides exponential tracking provided certain assumptions are met.

The developed control method takes a different approach to traditional LPV design

and provides a framework for future control design.

* Because the assumptions required of the controller are met by the HSV, a numerical

simulation was performed. After reducing the HSV nonlinear dynamics to that

of an LPV system motivation was provided to implement the controller designed.

A simulation is provided where the full nonlinear HSV dynamics are used. The

simulation demonstrates the efficacy of the proposed control design on this particular

HSV application. A wide range of temperature variations were used and tracking

control was implemented to demonstrate the performance of the controller.

* Further performance evaluation was conducted by designing an optimization proce-

dure to analyze the interplay between the HSV dynamics, temperature parameters,

and controller performance. A number of different temperature plant models for

HSV were near optimally tuned using a combination of a random search and evolv-

ing algorithms. Next, the control performance was evaluated and compared to the

other HSV temperature models. Comparative analysis is provided that suggests

regions where the temperature profiles of the HSV in conjunction with the proposed

control design achieve improved performance results. These results may provide

insight to structural systems designers for HSVs as well as provide scaffolding for

future numerical design optimization and control tuning.

6.3 Future Work

* The robust dynamic inversion control design in this dissertation requires knowledge

of the sign of the error signal derivative terms. While these measurements may be

available for specific applications, this underlying necessity reduces the generality of

the controller. Future work could focus on removing this restriction, and producing

an output feedback only robust dynamic inversion control.









* Another requirement of the control design is the requirement of the diagonal

dominance of the best guess feed forward input matrix. While this requirement is

not unreasonable because it only requires that the guess be within the vicinity of

the actual value, future work could focus on relaxing that requirement. Alleviating

this restriction could potentially be done by using partial adaptation laws while

simultaneously using robust algorithms to counter the parameter variations.

* It was shown that the controller developed is able to track inner-loop states for the

HSV, however it would be beneficial to adapt this inner loop control design to an

outer loop flight planning controller. In this way, more practical planned trajectories

can be tracked (e.g., altitude) by using the inner loop of pitch rate and pitch angle

control. Additionally, this same result can be attained by using backstepping

techniques. By backstepping through other state dynamics (e.g., altitude) and into

the control dynamics (e.g., pitch rate), a combined controller could be developed.

* The temperature and control gain optimization provides a good framework for

finding HSV designs with increased performance. It would be interesting in future

work to re-analyze the optimal control gain space, and see if it could be converged to

a smaller set. If the optimal set could be further converged, then through numerous

iterations a very precise and narrow range may be found. Finding a more optimal

design space may aid in future structural optimization searches.

* It would also be beneficial for the optimization work to have more accurate nonlinear

models. Obtaining better models will require working in collusion with HSV

designers. Getting high quality feedback on the design constraints and flight

trajectory constraints would further aid the search for optimality in regards to

control gains and temperature profiles. In addition, the dynamics could be modeled

and simulated with higher certainty if more details were known. Combining extra

data on the dynamics into the control design would help further the development of

actual flight worthy vehicles.









APPENDIX A
OPTIMIZATION DATA

The data presented in the following tables is the raw data from the images presented

in Chapter 5. The rows contains all of the Tos, in F and the columns contain the

Trtil in F. Empty spaces are places where the tail temperature is higher than the nose

temperature, and are outside the design space of this work and committed.












Table A-l: Total cost function, used to generate Figure 5-11 and 5-12 (Part 1)


Ttail F


250 300 350


112 ','7

134531
140t233

144110
140079
140945
1 11,'"1
138181
140t633
140904

149182


141681
146708
143591
14-, 1.-,
147113
147159
151291
1 1.;'','I ,
1440t27
143730

129812


14. 1'",
142439
1 1I; 17
140439
143303
1 1. .'. I.

145086
11'-27

145212
140633


400 450 500


140353
l1 11110
145178
139847
140785
144025
1 11,10
1401110.

142817
144027


149182
142468
139083
139202
145853
149182
139965
144790
140848
11'-27


141932
1 1.:.:ns
144040
1117 I -
129812
146527
1;- 110
140940
140110.


Table A-2: Total cost function, used to generate Figure 5-11 and 5-12 (Part 2)


TtailF


Tnose 550
oF


600 650


750 800 850


550 144322
600 144420 111"7
650 145109 141262 127435
700 140633 144027 111. 27 14011i0.
750 140 11.. 11.:.:'", 139825 140233 146708
800 144948 143418 145297 135394 142384 140069
850 111',.: 141883 148014 136336 1.:1',1 145803 145941
900 11.:';s 147566 129349 138888 131875 11'11", 1;: ',1 13403


144526
143210
141588
141588
1l1. I>
1;;.;17
143490
129673
14011h
143730
143730
1 1.; 1
146708
11 11.10
140) "1;
141959
1 1.;*' ,'I ,


145071
140254
140254
140577
145807
1 1. :. i,
141- .
1; ;' i11,1[
144033
145599
137784
142439
149182
146015
138801
145086


143199
129636
139825
141577
141863
137552
1;;" 1.; l
145621
1 Iii.;-'.
129812
139499
1 I:.:I
1 11110t












Table A-3: Control input cost function, used to generate Figure 5-7 and 5-8 (Part 1)


TtailF
T
nose 100 150 200 250 300 350 400 450 500

100 95951
150 95949 95953
200 95948 95949 95952
250 95948 95949 95952 95952
300 95949 95950 95953 95953 95949
350 95952 95952 95957 95957 95953 95953
400 95948 95953 95957 95953 95954 95952 95953
450 95951 ',L-.' 95952 95950 'i,-.' 95953 95952 95953
500 95950 95950 95954 95948 95953 95949 95952 95948 95953
550 95952 95950 95957 95949 95954 95952 95948 95937 95949
600 95952 95949 95950 95952 95951 95952 95948 95937 95952
650 95953 95952 95953 95953 95953 95954 95949 95953 95950
700 95954 95952 95953 '.'il1 95953 95953 95952 95953 95952
750 95952 95953 95952 95953 95953 95954 95950 95950 95954
800 95953 95953 95953 95949 95953 95952 95953 95952 95957
850 95952 95953 95953 95953 95953 95957 95953 95949 95953
900 95953 95953 95952 95953 95952 95953 95953 95954 95950







Table A-4: Control input cost function, used to generate Figure 5-7 and 5-8 (Part 2)

TtailOF
T
ose 550 600 650 700 750 800 850 900
OF
550 95952
600 95952 95952
650 95949 95952 95952
700 95953 95953 95954 95950
750 95950 95953 95957 95953 95954
800 95953 95949 95953 95949 95953 95949
850 95953 95952 95953 95952 95953 95952 95953
900 95948 95953 95951 95940 95949 95953 r.'l1i 95950












Table A-5: Error cost function, used to generate Figure 5-9 and 5-10 (Part 1)


T
nose 100 150 200 250 300 350 400 450 500
oF
100 1-.74
150 17_', I 49118
200 1.. 11.:1 1. 4 47444
250 1. 11..;' I l.: 14 47444 li.,.i -.
300 47136 11i_'. 47245 47942 I,117m.
350 37525 1',, 33679 38574 45727 47-, 1-
400 17. 11 4711.: 1.;',.7 44280 50754 11, 17 44400
450 33721 45337 l,_, 40418 7ID. 1 -., -.S *..:'
500 44516 43114 1'"11s 48162 1l,1-' 44490 1' 11,19 45979
550 47777 l-N-_' 41594 44129 51159 17.:.1 1.' 43146 47358
600 IT7 ,7 1i11l' 42479 11'i',- 51208 47673 11 7 .'2-.1 48088
650 47930 41831 1'i,.7 'li-, 55337 .:, 1 48075 49900 I -.:1
700 50754 1-. 17 44400 122'., 48002 49133 '..S ',.._22s 33860
750 l-.s -,.:' 33860 446.S0 48074 50572 44516 44015 50572
800 11-'2 50062 1.-. 1'. 44954 47776 33474 50911 1i.,7 ::'l" -'
850 46007 42848 46978 49969 1i'.:7. l'i. 1i.-. 44898 11' -1.
900 48002 49133 48658 ..:_S 33860 44G680 48074 50572 44516






Table A-6: Error cost function, used to generate Figure 5-9 and 5-10 (Part 2)

TtailOF
T
Those 550 600 650 700 750 800 850 900
OF
550 137
600 1 1i. 48905
650 49160 45310 311"-I
700 44S680 48074 50572 44516
750 44516 4711.: 1.;:,,7 44280 50754
800 48995 47469 1'. 1.: ;'i 1. l'i. 1.11 44120
850 1" ": 45931 52060 I.;'" 1 I -'I lI7';.- 1'""7
900 47880 51613 33397 42947 ::-.'--. 11 ..42 39514 38653












Table A-7: Pitch rate, peak-to-peak error, used to generate Figure 5-13 and 5-14 (Part 1)


TtailF


Tnose
F


0.1951
0.1678
0.2057
0.2057
0.1450
0.1374
0.1399
0.1535
0.2175
0 -2..;"
0.1738
0.1560
0.1336
0.1434
0.1510
0.1939
0.1539


150 200 250 300 350 400 450 500


0.1377
0.1722
0.1722
(I -2",.
0.1712
0.1530
0.2478
0.2197
0.1624
(i I I ".,
0.1327
0.1448
0.11.:,
0.1331
0.1468
0.1415


0.1421
0.1421
0.1365
0.1427
0.1500
0.1278
0.1505
0.1491
0.1 11'..
0.1857
0.1849
0.1530
0.1502
0.1532
0.1434


0.1842
0.1803
0.1372
0.1835
0.2214
0.1728
0.1430
0.1548
0.1553
0.2928
0.1573
0.1573
0.1532
0.11.:,


0.1669
0.1536
0.1336

0.1590
0.131.:
0.2071
0.1406
0.1539
0.1692
0.1595
0.1992
0.1530


0.1601
0.1448
0.1421
0.1672
0.1867
0.1394
0.1400
0.1415
0.1655
0.1916
0.1 11 .
0.1573


0.1849
0.1434
0.1481
0.1848
0.1799
0.1374
0.1434
0.2175
0.1655
0.1432
0.1688


0.1 1 .:1
0.2174
0.4458
0.4561
0.1665
0.11.':.
0.2200
0.1832
( 0.6, [
0.1655


0.1292
0.2287
0.1471
0.1530
0.1530
0.1655
0.1473
0.1409
0.2175


Table A-8: Pitch rate, peak-to-peak error, used to generate Figure 5-13 and 5-14 (Part 2)

TtailF


Tnose 5
550


600 650 700 750 800 850 900


0.1787
0.1960 0.1309
0.1719 0.1947 0.1353
0.1573 0.1692 0.1655 0.2175
0.2912 0.1530 0.1612 0.1835 0.1939
0.1471 0.2673 0.1356 0.1:-.1 0.1658 0.1833
0.1641 0.1323 0.1398 0.1507 0.1 1.. 0.1733 0.1395
0.1491 0.1 '.; 0.1499 0.3929 0.2276 0.1822 0.2941 0.2615











Table A-9: Pitch rate, steady-state peak-to-peak error, used to generate Figure 5-21 and
5-22 (Part 1)


TtailF


Tnose
0F


150 200 250 300 350 400 450 500


0.0170
0.0163
0.0179
0.0179
0.0176
0.0027
0.0232
0.0012
0.0053
0.0196
0.0173
0.0222
0.0186
0.0207
0.0165
0.0179
0.0182


0.0170
0.0156
0.0156
0.0166
0.0233
0.0169
0.0200
0.0048
0.0169
0.0186
0.0027
0.0154
0.0211
0.0213
0.0030
0.0202


0.0178
0.0178
0.0173
0.0016
0.0048
0.0173
0.0070
0.0036
0.0045
0.0164
0.0144
0.0008
0.0146
0.0161
0.0221


0.0163
0.0184
0.0028
0.0149
0.0031
0.0177
0.0166
0.0163
0.0152
0.0039
0.0154
0.0151
0.0170
0.0221


0.0167
0.0150
0.0186
0.0192
0.0166
0.0173
0.0178
0.0178
0.0172
0.0202
0.0163
0.0049
0.0008


0.0173
0.0154
0.0183
0.0166
0.0193
0.0183
0.0210
0.0191
0.0171
0.0029
0.0174
0.0160


0.0144
0.0221
0.0167
0.0159
0.0154
0.0173
0.0207
0.0053
0.0214
0.0150
0.0204


0.0221
0.0184
0.0032
0.0034
0.0202
0.0211
0.0056
0.0166
0.0176
0.0169


0.0160
0.0185
0.0185
0.0171
0.0008
0.0171
0.0050
0.0074
0.0053


Table A-10: Pitch rate, steady-state peak-to-peak error, used to generate Figure 5-21 and
5-22 (Part 2)


Ttail F


600 650 700 750 800 850 900


0.0210
0.0185
0.0202
0.0169
0.0187
0.0198
0.0179


0.0019
0.0171
0.001
0.0163
0.0199
0.0009


0.0053
0.0149
0.0026
0.0034
0.0058


0.0180
0.0170
0.0163
0.0015


0.0193
0.0204
0.0183


0.0173
0.0041


0.0021


Tnose
OF


0.0188
0.0180
0.0193
0.0154
0.0048
0.0171
0.0171
0.0226












Table A-11: Pitch rate, time to steady-state, used to generate Figure 5-17 and 5-18 (Part
1)

TtailF
T
1ose 100 150 200 250 300 350 400 450 500
oF
100 0.439
150 0.429 0.433
200 0.412 0.338 0.451
250 0.471 0.287 0.472 0.541
300 0.450 0.381 0.518 0.518 0.515
350 0.394 0.499 0.431 0.540 0.512 0.472
400 0.471 0..12 0.412 0.494 0.402 0.511 0.474
450 0.556 0.407 0.405 0.475 0.473 0.444 0. 1I- 0.519
500 0.580 0.613 0..12 0.450 0.424 0.473 0.404 0.1'", 0.473
550 0. 1.:'. 0.358 0.444 0.461 0.442 0.468 0.450 0.618 0.427
600 0.447 0. 1' 0.518 0.475 0.457 0.513 0.506 0.593 0.473
650 0.518 0.570 0.489 0.457 0.601 0.475 0.495 0.533 0.408
700 0.1--. 0.471 0.449 0.677 0.1',7 0.464 0.449 0.1'",1 0.450
750 0. 1'7 0. 1'11 2.143 0.474 0.453 0.445 0.564 0.692 0.470
800 0.442 0.491 0.450 0.471 0.494 0. 1l-' 0.473 0.495 0.587
850 0.432 0.576 0.470 0.527 0.593 0.464 0.477 0.517 0.572
900 0.494 0.1'12 0.1'7 0.427 0. 11. 0.1' 0.11.7 0.455 0.537





Table A-12: Pitch rate, time to steady-state, used to generate Figure 5-17 and 5-18 (Part
2)

TtailOF
Tns
ose 550 600 650 700 750 800 850 900
oF
550 0.450
600 0.421 0.476
650 0.453 0. I 1. 0.423
700 0.472 0.471 0.445 0.564
750 0.548 0.473 0.430 0.518 0.479
800 0.451 0.495 0.460 0.449 0.474 0.522
850 0.503 0.537 0.558 0.404 0.478 0.1,', 0.469
900 0.421 0.491 0.559 0.592 0.818 0.449 0.521 0.692












Table A-13: Velocity, peak-to-peak error, used to generate Figure 5-15 and 5-16 (Part 1)


TtailF
T
nose 100 150 200 250 300 350 400 450 500

100 1.5670
150 1.1. l'. 1 .1 17
200 1.611. 1.6669 1.5972
250 1.6445 1.6668 1.5973 1.5986
300 1.6839 1.6663 1.6344 1.6055 1.6081
350 1.5596 1.5904 1.5401 1.5366 1.5580 1.6235
400 1.5836 1.6022 1.4917 1.5910 1.6735 1.5357 1.5910
450 1.5634 1.7.-'1 1.-.'11 1.4420 1.7098 1.1lIII 1.5872 1.5456
500 1.111,' 1.11 .1 1.5321 1.5966 1.6579 1.1..;:' 1.5408 1.6075 1.5710
550 1.5893 1.7447 1.1-.' 1.6417 1.6516 1.6166 1.-.'7 1.4219 1.5953
600 1.6537 1.5934 1.5359 1.5993 1.7076 1.6038 1.5962 1.4238 1.5834
650 1.5961 1.6176 1.6366 1.612-' 1.4089 1.6170 1.7221 1.5990 1.6525
700 1.6735 1.5357 1.5910 1.4344 1.5949 1.5890 1.5876 1.5456 1.5294
750 1.5876 1.5456 1.5294 1.5980 1.6078 1.6965 1. 1i' 1.406t 1.6965
800 1.5948 1.6270 1.5828 1.6800 1.6248 1.5124 1.6033 1.6058 1.5128
850 1.5855 1.5205 1.'". I 1.6675 1.6205 1..1.:.; 1.5966 1.1~,.:1 1.6128
900 1.5940 1.5890 1.5872 1.5456 1.5294 1.5980 1.6078 1.6965 1.111i,






Table A-14: Velocity, peak-to-peak error, used to generate Figure 5-15 and 5-16 (Part 2)

TtailF
Tns
ose 550 600 650 700 750 800 850 900
OF
550 1.5814
600 1.'" 1., 1.'.7"..
650 1.6916 1.5845 1."7. I
700 1.5980 1.6078 1.6965 1.111'"1
750 1.4662 1.6022 1.-.-.1 1.5910 .1.61.:'
800 1.5951 1.6737 1.5572 1.1 '2 1.5873 1.6127
850 1.5830 1.5817 1.4735 1.5305 1.6027 1.5670 1.6405
900 1.6025 1.5693 1...12 1.4585 1.4581 1.6060 1.4721 1. i.











Table A-15: Velocity, steady-state peak-to-peak, used to generate Figure 5-23 and 5-24
(Part 1)


Ttail F


150 200 250


0.0037
0.0088 0.0050
0.0018 0.0036 0.001'
0.0019 0.0038 0.0047 0.0039
0.0037 0.0030 0.0034 0.0035
0.0016 0.0033 0.0010 0.0026
0.0035 0.0028 0.0013 0.0029
0.0002 0.0017 0.0027 0.0004
0.0021 0.0017 0.0015 0.0027
0.0069 0.0032 0.0012 0.0037
0.0038 0.0041 0.0022 0.0033
0.0035 0.0010 0.0070 0.0045
0.0059 0.0021 0.0031 0.0024
0.0014 0.0040 0.0008 0.0029
0.0035 0.0094 0.0038 0.0034
0.0028 0.0003 0.0075 0.0068
0.0023 0.0031 0.0031 0.0008


300 350 400 450 500


0.0131
0.0015 0.0066
0.0059 0.0021 0.0031
0.0105 0.0032 0.0014 0.0031
0.0037 0.0069 0.0022 0.0014 0.0027
0.0103 0.0022 0.001'. 0.0026 0.0035
0.0108 0.0018 0.0038 0.0027 0.0035
0.0041 0.0051 0.0040 0.0084 0.0066
0.0028 0.0039 0.0014 0.0040 0.0008
0.0037 0.0055 0.0021 0.0022 0.0055
0.0118 0.0009 0.0126 0.0033 0.0008
0.0006 0.0045 0.0024 0.0023 0.0016
0.0031 0.0040 0.0099 0.0021 0.0027


Table A-16: Velocity, steady-state peak-to-peak, used to generate Figure 5-23 and 5-24
(Part 2)


TtailF


Tnose 5
550


600 650 700 750 800 850 900


0.0022
0.0040 0.0027
0.0034 0.0036 0.0008
0.0029 0.0037 0.0055 0.0021
0.0016 0.0028 0.0013 0.0029 0.0041
0.0035 0.0054 0.0032 0.0015 0.0030 0.0028
0.0027 0.0007 0.0013 0.0018 0.0101 0.0041 0.0057
0.0027 0.0033 0.0002 0.0006 0.0005 0.0107 0.0005 0.0009












Table A-17: Velocity, time to steady-state, used to generate Figure 5-19 and 5-20 (Part 1)

TtailF
T
1ose 100 150 200 250 300 350 400 450 500
oF
100 2.012
150 1.119 0.915
200 0.539 0 2'i. 0. 1'1.
250 0.498 0 1 0.528 0.506
300 0.' 1.: 0.314 0.586 0. 1'' 1.201
350 0.383 0.520 0.474 0.472 0.522 0.701
400 0.501 0.515 0.403 0.1',' 0.355 1.94 0.516
450 0.747 0.521 0.491 0.821 0.-.1.' 0.491 0.513 0.637
500 0.494 0.472 0.484 0.983 0.378 0.502 0.481 0. 12 0.656
550 0. 1'' 0.339 0.385 0.,11 0.568 1.208 0. 1'i. 0.841 1.201
600 0.562 0.500 0. 1". 0.563 0.578 1.01.: 0.1'1' 0.708 0.712
650 0.562 1.681 0.627 0.400 0.705 0.521 1.396 1.760 0.932
700 0.383 0. 1"r 0.704 0.808 0.836 0.491 0.504 0.516 3.330
750 0.459 0.587 3.300 1.347 0.539 0.679 0.473 0. 1',, 0.680
800 0.678 0.817 0.459 0.538 1.114 0.309 0.929 0.675 0.403
850 0.702 1.356 0.522 0.776 0.514 0.430 0.541 0.511 0.363
900 0.798 0.480 0.519 0.632 2.844 0.822 0.'1.' 0.607 0.518







Table A-18: Velocity, time to steady-state, used to generate Figure 5-19 and 5-20 (Part 2)

TtailOF
Tns
ose 550 600 650 700 750 800 850 900
oF
550 0.473
600 0.398 0.568
650 0.515 0.516 0.519
700 0.821 0.520 0.679 0.473
750 0.518 0.541 0.473 0.541 0.671
800 0.467 0.705 0.702 0.337 0.802 0.550
850 0.-.12 0.564 0.518 0.300 0.688 0.818 0.607
900 0.474 0.545 2.293 0.564 1.095 1.393 0.i.12 0.559









REFERENCES


[1] L. K. Abbasa, C. Qian, P. Marzocca, G. Zafer, and A. Mostafa, "Active aerother-
moelastic control of hypersonic double-wedge lifting surface," Chin. J. Aeronaut.,
vol. 21, pp. 8-18, 2008.

[2] P. Apkarian and P. Gahinet, "A convex characterisation of gain-scheduled h-inf
controllers," IEEE Transactions on Automatic Control, vol. 40, pp. 853 P i;1, 1995.

[3] P. Apkarian and H. D. Tuan, "Parameterized Imis in control theory," SIAM J. Contr.
Optim., vol. 38, no. 4, pp. 1241-1264, 2000.

[4] K. J. Austin and P. A. Jacobs, "Application of genetic algorithms to hypersonic flight
control," in IFSA World Congr., NAFIPS Int. Conf., Vancouver, British Columbia,
Canada, July 2001, pp. 2428-2433.

[5] G. J. Balas, "Linear parameter varying control and its application to a turbofan
engine," Int. J. Non-linear and Robust Control, Special issue on Gain Scheduled
Control, vol. 12, no. 9, pp. 763-798, 2002.

[6] S. Bhat and R. Lind, "Control-oriented analysis of thermal gradients for a hypersonic
vehicle," in Proc. IEEE Am. Control Conf., 2009.

[7] J. M. Biannie and P. Apkarian, "Missile autopilot design via a modified lpv synthesis
technique," Aerosp. Sci. Technol., pp. 763-798, 1999.

[8] M. Bolender and D. Doman, "A non-linear model for the longitudinal dynamics of
a hypersonic air-breathing vehicle," in Proc. AIAA Guid. Navig. Control Conf., San
Francisco, CA, Aug. 2005.

[9] "Modeling unsteady heating effects on the strucutral dynamics of a hypersonic
vehicle," in AIAA Atmos. Flight Mech. Conf., Keystone, CO, Aug. 2006.

[10] M. A. Bolender and D. B. Doman, "Nonlinear longitudinal dynamical model of an
air-breathing hypersonic vehicle," J. Spacecraft Rockets, vol. 44, no. 2, pp. 374-387,
Apr. 2007.

[11] S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in
system and control theory. SIAM, 1994.

[12] A. J. Culler, T. Williams, and M. A. Bolender, "Aerothermal modeling and dynamic
analysis of a hypersonic vehicle," in AIAA Atmos. Flight Mech. Conf., Hilton Head,
SC, Aug. 2007.

[13] J. Daafouz, J. Bernussou, and J. C. Geromel, "On inexact lpv control design of
continuous-time polytopic systems," IEEE Transactions on Automatic Control, vol.
Vol. 53, No. 7, no. 7, August 2008.









[14] M. Dinh, G. Scorletti, V. Fromion, and E. Magarotto, "Parameter dependent h-inf
control by finite dimensional lmi optimization: application to trade-off dependent
control," Int. J. Robust Nonlinear Contr., vol. 15, no. 9, pp. 383-406, 2005.

[15] A. Filippov, Differential equations with discontinuous right-hand side. Netherlands:
Kluwer Academic Publishers, 1988.

[16] L. Fiorentini, A. Serrani, M. A. Bolender, and D. B. Doman, "Nonlinear ro-
bust/adaptive controller design for an air-breathing hypersonic vehicle model," in
Proc. AIAA Guid. Navig. Control Conf., Hilton Head, SC, Aug. 2007.

[17] "Nonlinear robust adaptive control of flexible air-breathing hypersonic vehicle,"
J. Guid. Contr. Dynam., vol. 32, No. 2, pp. 402-417, April 2009.

[18] P. Gaspa, I. Szaszi, and J. Bokor, "Active suspension design using linear parameter
varying control," Int. J. Vehicle Auton. Syst., vol. 1, no. 2, pp. 206-221, 2003.

[19] T. Gibson, L. Crespo, and A. Annaswamy, "Adaptive control of hypersonic vehicles
in the presence of modeling uncertainties," in Proc. IEEE Am. Control Conf., June
2009.

[20] S. Gutman, "Uncertain dynamical systems-a Lyapunov min-max approach," IEEE
Trans. Autom. Control, vol. 24, no. 3, pp. 437-443, 1979.

[21] J. Heeg, T. A. Zeiler, A. S. Pototzky, and C. V. Spain, "Aerothermoelastic analysis of
a NASP demonstrator model," in Proc. of the AIAA Structures Struct. Dyn. Mater.
Conf., La Jolla, CA, Apr. 1993.

[22] A. Helmersson, "mu synthesis and lft scheduling with mixed uncertainties," in
Proceedings of the European Control Conference, 1995, pp. 153-158.

[23] P. Jankovsky, D. O. Sigthorsson, A. Serrani, and S. Yurkovich, "Output feedback
control and sensor placement for a hypersonic vehicle model," in Proc. AIAA Guid.
Navig. Control Conf., Hilton Head, SC, Aug. 2007.

[24] 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.

[25] L. Li and V. A. Ugrinovshii, "Robust stabilization of lpv systems with structured
uncertainty using minimax controllers," in IEEE Conference on Decision and Control,
2007.

[26] R. Lind, "Linear parameter-varying modeling and control of structural dynamics
with aerothermoelastic effects," J. Guid. Contr. Dynam., vol. 25, no. 4, pp. 733-739,
July-Aug. 2002.









[27] W. MacKunis, K. Kaiser, Z. D. Wilcox, and W. E. Dixon, "Global adaptive output
feedback tracking control of an unmanned aerial vehicle," IEEE Trans. Control Syst.
Technol., to appear, 2010.

[28] W. MacKunis, M. K. Kaiser, P. M. Patre, and W. E. Dixon, "Adaptive dynamic
inversion for asymptotic tracking of an aircraft reference model," in Proc. AIAA
Guid. Navig. Control Conf., Honolulu, HI, 2008.

[29] W. MacKunis, Z. D. Wilcox, K. Kaiser, and W. E. Dixon, "Global adaptive output
feedback MRAC," in Proc. IEEE Conf. Decis. Control Chin. Control Conf., 2009.

[30] C. I. Marrison and R. F. Stengel, "Design of robust control systems for a hypersonic
aircraft," J. Guid. Contr. Dynam., vol. 21, no. 1, pp. 58-63, 1998.

[31] A. Packard, "Gain scheduling via linear fractional transformations," Systems &
Control Letters, vol. 22, no. 2, pp. 79-92, 1994.

[32] J. T. Parker, A. Serrani, S. Yurkovich, M. A. Bolender, and D. B. Doman, "Control-
oriented modeling of an air-breathing hypersonic vehicle," J. Guid. Contr. Dynam.,
vol. 30, no. 3, pp. 856-869, 2007.

[33] P. Patre, W. Mackunis, M. Johnson, and W. Dixon, "Composite adaptive control for
Euler-Lagrange systems with additive disturbances," Automatica, vol. 46, no. 1, pp.
140-147, 2010.

[34] P. M. Patre, K. Dupree, W. MacKunis, and W. E. Dixon, "A new class of modular
adaptive controllers, part II: Neural network extension for non-LP systems," in Proc.
IEEE Am. Control Conf., 2008, pp. 1214-1219.

[35] P. M. Patre, W. MacKunis, K. Dupree, and W. E. Dixon, "A new class of modular
adaptive controllers, part I: Systems with linear-in-the-parameters uncertainty," in
Proc. IEEE Am. Control Conf., 2008, pp. 1208-1213.

[36] RISE-Based Robust and Adaptive Control of Nonlinear Systems. Boston:
Birkhauser, 2009, under contract.

[37] P. M. Patre, W. MacKunis, K. Kaiser, and W. E. Dixon, "Asymptotic tracking
for uncertain dynamic systems via a multilayer neural network feedforward and
RISE feedback control structure," IEEE Trans. Autom. Control, vol. 53, no. 9, pp.
2180-2185, 2008.

[38] P. M. Patre, W. Mackunis, C. Makkar, and W. E. Dixon, "Asymptotic tracking for
systems with structured and unstructured uncertainties," in IEEE Transactions on
Control Systems Technology, vol. 16, No. 2, 2008, pp. 373-379.

[39] P. Pellanda, P. Apkarian, and H. D. Tuan, "Missile autopilot design via a multi-
channel Ift/lpv control method," Int. J. Robust Nonlinear Control, vol. 12, pp. 1-20,
2002.









[40] Z. Qu and J. X. Xu, 'i\ hhl -1-based learning controls and their comparisons using
Lyapunov direct method," Asian J. Control, vol. 4(1), pp. 99-110, 2002.

[41] A. Serrani, A. Zinnecker, L. Fiorentini, M. Bolender, and D. Doman, "Integrated
adaptive guidance and control of constrained nonlinear air-breathing hypersonic
vehicle models," in Proc. IEEE Am. Control Conf., June 2009.

[42] J. Shamma and M. Athans, "Gain scheduling: Potential hazards and possible
remedies," IEEE Control System Magazine, vol. vol. 12, no. no. 3, pp. pp. 101-107,
1992.

[43] J. S. Shamma, "Gain-scheduling missile autopilot design using linear parameter
varying transformations," Journal of Guidance, Control and Dynamics, vol. 16, no. 2,
pp. 256-263, 1993.

[44] D. Sigthorsson, P. Jankovsky, A. Serrani, S. Yurkovich, M. Bolender, and D. Doman.,
"Robust linear output feedback control of an air-breathing hypersonic vehicle," J.
Guid. Contr. Dynam., vol. 31, No. 4, pp. 1052-1066, July 2008.

[45] M. Spillman, P. Blue, S. Banda, and L. Lee, "A robust gain-scheduling example using
linear parameter-varying feedback," in Proceedings of the IFAC 13th Triennial World
Congress, 1996, pp. 221-226.

[46] K. Astr6m, T. Hagglund, C. Hang, and W. Ho, "Automatic tuning and adaptation
for PID controllers-a survey," Control Eng. Pract., vol. 1, no. 4, pp. 669-714, 1993.

[47] L. F. Vosteen, "Effect of temperature on dynamic modulus of elasticity of some
structural allows," in Tech. Rep. 43Y8, Loi!/lleY Aeronoutical Laboratory, Hampton,
VA, August 1958.

[48] S. A. W.A., "Strong stabilization of mimo systems: An Imi approach," in Systems,
Signals and Devices, 2009.

[49] Q. Wang and R. F. Stengel, "Robust nonlinear control of a hypersonic aircraft," J.
Guid. Contr. Dynam., vol. 23, no. 4, pp. 577-585, 2000.

[50] Z. D. Wilcox, S. Bhat, R. Lind, and W. E. Dixon, "Control performance variation
due to aerothermoelasticity in a hypersonic vehicle: Insights for structural design," in
Proc. AIAA Guid. Navig. Control Conf., August 2009.

[51] Z. D. Wilcox, W. MacKunis, S. Bhat, R. Lind, and W. E. Dixon, "Robust nonlinear
control of a hypersonic aircraft in the presence of aerothermoelastic effects," in Proc.
IEEE Am. Control Conf., St. Louis, MO, June 2009, pp. 2533-2538.

[52] "Lyapunov-based exponential tracking control of a hypersonic aircraft with
aerothermoelastic effects," J. Guid. Contr. Dynam., vol. 33, no. 4, Jul./Aug 2010.

[53] T. Williams, M. A. Bolender, D. B. Doman, and O. Morataya, "An aerothermal
flexible mode analysis of a hypersonic vehicle," in AIAA Paper 2006-6647, Aug. 2006.









[54] B. Xian, D. M. Dawson, M. S. de Queiroz, and J. Chen, "A continuous asymptotic
tracking control strategy for uncertain nonlinear systems," IEEE Trans. Autom.
Control, vol. 49, no. 7, pp. 1206-1211, Jul. 2004.

[55] M. Yoshihiko, "Adaptive gain-scheduled H-infinity control of linear parameter-varying
systems with nonlinear components," in Proc. IEEE Am. Control Conf., Denver, CO,
June 2003, pp. 208-213.









BIOGRAPHICAL SKETCH

Zach Wilcox grew up in Yarrow Point, a city just outside of Seattle, Washington, and

lived there until moving to Florida to attend college in 2001. He received dual Bachelor

of Science degrees from the University of Florida's Aerospace and Mechanical Engineering

department in the spring of 2006. During his undergraduate work, Zach participated as a

diver on UF's Men's Swimming Diving Team. In addition, he did research work for UF's

Micro Air Vehicle (MAV) group and participated in International MAV competitions. He

recieved his Masters of Science in Aerospace Engineering from University of Florida in the

spring of 2008. His Doctoral studies were in the Nonlinear Controls and Robotics Group

in the Department of Mechanical and Aerospace Engineering under the advisement of Dr.

Dixon. He received his Ph.D. in Aerospace Engineering in August 2010.





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NONLINEARCONTROLOFLINEARPARAMETERVARYINGSYSTEMSWITH APPLICATIONSTOHYPERSONICVEHICLES By ZACHARYDONALDWILCOX ADISSERTATIONPRESENTEDTOTHEGRADUATESCHOOL OFTHEUNIVERSITYOFFLORIDAINPARTIALFULFILLMENT OFTHEREQUIREMENTSFORTHEDEGREEOF DOCTOROFPHILOSOPHY UNIVERSITYOFFLORIDA 2010 1

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c 2010ZacharyDonaldWilcox 2

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Thisworkisdedicatedtomyparents,family,friends,andadvisor,whohaveprovidedme withsupportduringthechallengingmomentsinthisdissertationwork. 3

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ACKNOWLEDGMENTS Iwouldliketoexpresssinceregratitudetomyadvisor,Dr.WarrenE.Dixon,who isapersonwithremarkablea ability.Asanadvisor,heprovidedthenecessaryguidance andallowedmetodevelopmyownideas.Asamentor,hehelpedmeunderstandthe intricaciesofworkinginaprofessionalenvironmentandhelpeddevelopmyprofessional skills.Ifeelfortunateingettingtheopportunitytoworkwithhim. 4

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TABLEOFCONTENTS page ACKNOWLEDGMENTS .................................4 LISTOFTABLES........................... ..........7 LISTOFFIGURES...... .................... ..........8 ABSTRACT........................................10 CHAPTER 1INTRODUCTION.... .................... ..........12 1 .1MotivationandProblemStatement......................12 1.2OutlineandContributions...........................16 2LYAPUNOV-BASEDEXPONENTIALTRACKINGCONTROLOFLPVSYSTEMSWITHANUNKNOWNSYSTEMMATRIX,UNCERTAININPUTMATRIXVIADYNAMICINVERSION........................19 2.1Introduction ...................................19 2.2LinearParameterVaryingModel.......................21 2.3ControlDevelopm ent..............................23 2.3.1ControlObjective............................23 2.3.2Open-LoopErrorSystem........................24 2.3.3Closed-LoopErrorSystem.......................25 2.4StabilityAnalysi s................. ...............27 2.5Conclusions..... .................... ..........30 3HYPERSONICVEHICLEDYNAMICSANDTEMPERATUREMODEL...32 3.1Introduction ...................................32 3.2RigidBodyandElasticDynamics.......................32 3.3TemperaturePro leModel...........................33 3.4Conclusion...... .................... ..........38 4LYAPUNOV-BASEDEXPONENTIALTRACKINGCONTROLOFAHYPERSONICAIRCRAFTWITHAEROTHERMOELASTICEFFECTS.....39 4.1Introduction ...................................39 4.2HSVModel..... .................... ..........41 4.3ControlObjectiv e................. ...............42 4.4SimulationResult s...............................44 4.5Conclusion...... .................... ..........48 5

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5CONTROLPERFORMANCEVARIATIONDUETONONLINEARAEROTHERMOELASTICITYINAHYPERSONICVEHICLE:INSIGHTSFORSTRUCTURALDESIGN..... .................... ..........53 5.1Introduction ...................................53 5.2DynamicsandController............................54 5.3OptimizationviaRandomSearchandEvolvingAlgorithms.........55 5.4ExampleCase.... .................... ..........57 5.5Results............................ ..........61 5.6Conclusion...... .................... ..........73 6CONCLUSIONSANDFUTUREWORK......................75 6.1Conclusions..... .................... ..........75 6.2Contributions ..................................76 6.3FutureWork..... .................... ..........77 APPENDIX AOPTIMIZATIONDAT A...............................79 REFERENCES......... .................... ..........89 BIOGRAPHICALSKETCH ................. ...............94 6

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LISTOFTABLES Table page 3-1Naturalfrequenciesfor5lineartemperaturepro les(Nose/Tail)indegreesF. Percentdi erenceisthedi erencebetweenthemaximumandminimumfrequenciesdividedbytheminimumfrequency.....................36 5-1OptimizationControlGainSearchStatistics....................73 A-1Totalcostfunction,usedtogenerateFigure5-11and5-12(Part1).......80 A-2Totalcostfunction,usedtogenerateFigure5-11and5-12(Part2).......80 A-3Controlinputcostfunction,usedtogenerateFigure5-7and5-8(Part1)....81 A-4Controlinputcostfunction,usedtogenerateFigure5-7and5-8(Part2)....81 A-5Errorcostfunction,usedtogenerateFigure5-9and5-10(Part1)........82 A-6Errorcostfunction,usedtogenerateFigure5-9and5-10(Part2)........82 A-7Pitchrate,peak-to-peakerror,usedto generateFigure5-13and5-14(Part1).83 A-8Pitchrate,peak-to-peakerror,usedto generateFigure5-13and5-14(Part2).83 A-9Pitchrate,steady-statepeak-to-peakerror,usedtogenerateFigure5-21and522(Part1)............................ ..........84 A-10Pitchrate,steady-statepeak-to-peakerror,usedtogenerateFigure5-21and522(Part2)............................ ..........84 A-11Pitchrate,timetosteady-state,usedtogenerateFigure5-17and5-18(Part1)85 A-12Pitchrate,timetosteady-state,usedtogenerateFigure5-17and5-18(Part2)85 A-13Velocity,peak-to-peakerror,usedtogenerateFigure5-15and5-16(Part1)..86 A-14Velocity,peak-to-peakerror,usedtogenerateFigure5-15and5-16(Part2)..86 A-15Velocity,steady-statepeak-to-peak,usedtogenerateFigure5-23and5-24(Part 1)....................... ....................87 A-16Velocity,steady-statepeak-to-peak,usedtogenerateFigure5-23and5-24(Part 2)....................... ....................87 A-17Velocity,timetosteady-state,usedtogenerateFigure5-19and5-20(Part1)..88 A-18Velocity,timetosteady-state,usedtogenerateFigure5-19and5-20(Part2)..88 7

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LISTOFFIGURES Figure page 3-1Modulusofelasticityforthe rstthreedynamicmodesofvibrationforafreefreebeamoftitanium .................. ...............34 3-2Frequenciesofvibrationforthe rstthreedynamicmodesofafree-freetitaniumbeam............................. ..........35 3-3NineconstanttemperaturesectionsoftheHSVusedfortemperaturepro le modeling.......... .................... ..........35 3-4Lineartemperaturepro lesusedtocalculatevaluesshowninTable3-1......37 3-5Asymetricmodeshapesforthehypersonicvehicle.Thepercentdi erencewas calculatedbasedonthemaximumminustheminimumstructuralfrequencies dividedbytheminimu m................. ...............37 4-1Temperaturevariationfortheforebodyandaftbodyofthehypersonicvehicle asafunctionoftime. .................................45 4-2Inthis gure, denotesthe elementinthedisturbancevecor .Disturbances fromtoptobottom:velocity ,angleofattack ,pitchrate ,the 1elasticstructuralmode 1,the 2elasticstructuralmode 2,andthe 3elastic structuralmode 3,asdescribedin(411)......................46 4-3Referencemodelouputs ,whicharethedesiredtrajectoriesfortop:velocity ( ) ,middle:angleofattack ( ) ,andbottom:pitchrate ( ) .......47 4-4Top:velocity ( ) ,bottom:velocitytrackingerror ( ) .............48 4-5Top:angleofattack ( ) ,bottom:angleofattacktrackingerror ( ) ......49 4-6Top:pitchrate ( ) ,bottom:pitchratetrackingerror ( ) ..........49 4-7Top:fuelequivalenceratio .Middle:elevatorde ection .Bottom:Canard de ection ................... ....................50 4-8Top:altitude ( ) ,bottom:pitchangle ( ) ...................50 4-9Top: 1structuralelasticmode 1.Middle:2structuralelasticmode 2.Bottom: 3structuralelasticmode 3..........................51 5-1HSVsurfacetemperaturepro les. [450 900 ] ,and [100 800 ] .54 5-2Desiredtrajectories:pitchrate (top)andvelocity (bottom).........58 5-3Disturbancesforvelocity (top),angleofattack (secondfromtop),pitch rate (secondfrombottom)andthe 1structuralmode(bottom)........58 8

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5-4Trackingerrorsforthepitchrate indegrees/sec(top)andthevelocity in ft/sec(bottom). ....................................59 5-5Controlinputsfortheelevator indegrees(top)andthefuelratio (bottom).60 5-6Costfunctionvaluesforthetotalcost (top),theinputcost (middle) andtheerrorcost (bottom)...........................60 5-7Controlcostfunction dataasafunctionoftailandnosetemperatureproles.................. .........................62 5-8Controlcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les.................. ....................62 5-9Errorcostfunction dataasafunctionoftailandnosetemperaturepro les.63 5-10Errorcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les.......................................63 5-11Totalcostfunction dataasafunctionoftailandnosetemperaturepro les.64 5-12Totalcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les.......................................65 5-13Peak-to-peaktransienterrorforthepitchrate ( ) trackingerrorindeg./sec...66 5-14Peak-to-peaktransienterror( ltered)forthepitchrate ( ) trackingerrorin deg./sec........... .................... ..........66 5-15Peak-to-peaktransienterrorforthevelocity ( ) trackingerrorinft/sec.....67 5-16Peak-to-peaktransienterror( ltered)forthevelocity ( ) trackingerrorin ft./sec............ .................... ..........67 5-17Timetosteady-stateforthepitchrate ( ) trackingerrorinseconds.. .....68 5-18Timetosteady-state( ltered)forthepitchrate ( ) trackingerrorinseconds.68 5-19Timetosteady-stateforthevelocity ( ) trackingerrorinseconds........69 5-20Timetosteady-state( ltered)forthevelocity ( ) trackingerrorinseconds..69 5-21Steady-statepeak-to-peakerrorforthepitchrate ( ) indeg./sec.........70 5-22Steady-statepeak-to-peakerror( ltered)forthepitchrate ( ) indeg./sec....71 5-23Steady-statepeak-to-peakerrorforthevelocity ( ) inft./sec...........71 5-24Steady-statepeak-to-peakerror( ltered)forthevelocity ( ) inft./sec.....72 5-25Combinedoptimization chartofthecontrolanderrorcosts,transientand steady-statevalues. .................................. 7 3 9

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AbstractofDissertationPresentedtotheGraduateSchool oftheUniversityofFloridainPartialFul llmentofthe RequirementsfortheDegreeofDoctorofPhilosophy NONLINEARCONTROLOFLINEARPARAMETERVARYINGSYSTEMSWITH APPLICATIONSTOHYPERSONICVEHICLES By ZacharyDonaldWilcox August2010 Chair:WarrenE.Dixon Major:AerospaceEngineering Thefocusofthisdissertationistodesignacontrollerforlinearparametervarying (LPV)systems,applyitspeci callytoair-breathinghypersonicvehicles,andexaminethe interplaybetweencontrolperformanceandthestructuraldynamicsdesign.Speci callya Lyapunov-basedcontinuousrobustcontrollerisdevelopedthatyieldsexponentialtracking ofareferencemodel,despitethepresenceofbounded,nonvanishingdisturbances.The hypersonicvehiclehastimevaryingparameters,speci callytemperaturepro les,andits dynamicscanbereducedtoanLPVsystemwithadditivedisturbances.SincetheHSV canbemodeledasanLPVsystemtheproposedcontroldesignisdirectlyapplicable.The controlperformanceisdirectlyexaminedthroughsimulations. Awidevarietyofapplicationsexistthatcanbee ectivelymodeledasLPVsystems. Inparticular, ightsystemshavehistoricallybeenmodeledasLPVsystemsandassociated controltoolshavebeenappliedsuchasgain-scheduling,linearmatrixinequalities(LMIs), linearfractionaltransformations(LFT),and -types.However,asthetypeof ight environmentsandtrajectoriesbecomemoredemanding,thetraditionalLPVcontrollers maynolongerbesu cient.Inparticular,hypersonic ightvehicles(HSVs)presentan inherentlydi cultproblembecauseofthenonlinearaerothermoelasticcouplinge ectsin thedynamics.HSV ightconditionsproducetemperaturevariationsthatcanalterboth thestructuraldynamicsand ightdynamics.Startingwiththefullnonlineardynamics, theaerothermoelastice ectsaremodeledbyatemperaturedependent,parametervarying 10

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state-spacerepresentationwithaddeddist urbances.Themodelincludesanuncertain parametervaryingstatematrix,anuncert ainparametervaryingnon-square(column de cient)inputmatrix,andanadditiveboundeddisturbance.Inthisdissertation,a robustdynamiccontrollerisformulatedforauncertainanddisturbedLPVsystem.The developedcontrolleristhenappliedtoaHSVmodel,andaLyapunovanalysisisusedto proveglobalexponentialreferencemodeltrackinginthepresenceofuncertaintyinthe stateandinputmatricesandexogenousdisturbances.Simulationswithaspectrumof gainsandtemperaturepro lesonthefullnonlineardynamicmodeloftheHSVisusedto illustratetheperformanceandrobustnessofthedevelopedcontroller. Inaddition,thisworkconsidershowtheperformanceofthedevelopedcontroller variesoverawidevarietyofcontrolgainsandtemperaturepro lesandareoptimized withrespecttodi erentperformancemetrics.Speci cally,varioustemperaturepro le modelsandrelatednonlineartemperaturedependentdisturbancesareusedtocharacterize therelativecontrolperformanceande ortforeachmodel.Examiningsuchmetricsas afunctionoftemperatureprovidesapotentialinroadtoexaminetheinterplaybetween structural/thermalprotectiondesignandcontroldevelopmentandhasapplicationfor futureHSVdesignandcontrolimplementation. 11

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CHAPTER1 INTRODUCTION 1.1MotivationandProblemStatement RecentresearchonnonlinearinversionoftheinputdynamicsbasedonLyapunov stabilitytheoryprovidesasteppingstonetoLPVdynamicinversion.In[27,28],dynamic inversiontechniquesareusedtodesigncontrollersthatcanadaptivelyandrobustly stabilizestate-spacesystemswithuncertainconstantparametersandadditiveunknown boundeddisturbances.However,thisworkislimitedtotime-invarientparametersand thereforeisnotapplicabletoLPVsystems.Theworkpresentedinthischapterisan extensionoftheworkin[27,28],andprovidesacontinuousrobustcontrollerthatisable tostabilizegeneralperturbedLPVsystemswithdisturbances,whenboththestate,input matrices,time-varyingparameters,anddisturbancesareunknown. ThedesignofguidanceandcontrolsystemsforairbreathingHSVischallengingbecausethedynamicsoftheHSVarecomplexandhighlycoupledasin[10],and temperature-inducedsti nessvariationsimpactthestructuraldynamicssuchasin[21]. Muchofthisdi cultyarisesfromtheaerodynamic,thermodynamic,andelasticcoupling (aerothermoelasticity)inherentinHSVsystems.BecauseHSVtravelatsuchhighvelocities(inexcessofMach5)therearelargeamountsofaerothermalheating.Aerothermal heatingisnon-uniform,generallyproducin gmuchhighertemperatu resatthestagnation pointofair ownearthefrontofthevehicle.Coupledwithadditionalheatingdueto theengine,HSVshavelargethermalgradientsbetweenthenoseandtail.Thestructural dynamics,inturn,a ecttheaerodynamicproperties.Vibrationintheforwardfuselage changestheapparentturnangleofthe ow,whichresultsinchangesinthepressure distributionovertheforebodyoftheaircraft.Theresultingchangesinthepressuredistributionovertheaircraftmanifestthemselvesasthrust,lift,drag,andpitchingmoment perturbationsasin[10].TodevelopcontrollawsforthelongitudinaldynamicsofaHSV 12

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capableofcompensatingforthesestructuralandaerothermoelastice ects,structural temperaturevariationsandstructu raldynamicsmustbeconsidered. Aerothermoelasticityistheresponseofelasticstructurestoaerodynamicheatingand loading.Aerothermoelastice ectscannotbeignoredinhypersonic ight,becausesucheffectscandestabilizetheHSVsystemasin[21].Alossofsti nessinducedbyaerodynamic heatinghasbeenshowntopotentiallyinducedynamicinstabilityinsupersonic/hypersonic ightspeedregimesasin[1].Yetactivecontrolcanbeusedtoexpandthe utterboundaryandconvertunstablelimitcycleoscillations(LCO)tostableLCOasshownin[1].An activestructuralcontrollerwasdevelopedin[26],whichaccountsforvariationsintheHSV structuralpropertiesresultingfromaerothermoelastice ects.Thecontroldesignin[26] modelsthestructuraldynamicsusingaLPVframework,andstatesthebene tstousing theLPVframeworkaretwo-fold:thedynamicscanberepresentedasasinglemodel,and controllerscanbedesignedthathavea nedependencyontheoperatingparameters. Previouspublicationshaveexaminedthechallengesassociatedwiththecontrol ofHSVs.Forexample,HSV ightcontrollersaredesignedusinggeneticalgorithmsto searchadesignparameterspacewherethenonlinearlongitudinalequationsofmotion containuncertainparametersasin[4,30,49 ].SomeofthesedesignsutilizeMonteCarlo simulationstoestimatesystemrobustnessateachsearchiteration.Anotherapproach [4]istousefuzzylogictocontroltheattitudeoftheHSVaboutasinglelowend ight condition.Whilesuchapproachesasin[4,30,49]generatestabilizingcontrollers,the proceduresarecomputationallydemandingandrequiremultipleevaluationsimulations oftheobjectivefunctionandhavelargeconvergenttimes.Anadaptivegain-scheduled controllerin[55]wasdesignedusingestimatesofthescheduledparameters,andasemioptimalcontrollerisdevelopedtoadaptivelyattain controlperformance.This controlleryieldsuniformlyboundedstabilityduetothee ectsofapproximationerrors andalgorithmicerrorsintheneuralnetworks.Feedbacklinearizationtechniqueshave beenappliedtoacontrol-orientedHSVmodeltodesignanonlinearcontrollerasin[32]. 13

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Themodelin[32]isbasedonapreviouslydevelopedHSVlongitudinaldynamicmodel in[8].Thecontroldesignin[32]neglectsvariationsinthrustliftparameters,altitude, anddynamicpressure.Linearoutputfeedbacktrackingcontrolmethodshavebeen developedin[44],wheresensorplacementstrategiescanbeusedtoincreaseobservability, orreconstructfullstateinformationforas tate-feedbackcontroller.Arobustoutput feedbacktechniqueisalsodevelopedforthelinearparameterizableHSVmodel,which doesnotrelyonstateobservation.Arobustsetpointregulationcontrollerin[17]is designedtoyieldasymptoticregulationinthepresenceofparametricandstructural uncertaintyinalinearpar ameterizableHSVsystem. Anadaptivecontrollerin[19]wasdesignedtohandle(linearintheparameters) modelinguncertainties,actuatorfailures,andnon-minimumphasedynamicsasin[17] foraHSVwithelevatorandfuelratioinputs.Anotheradaptiveapproachin[41]was recentlydevelopedwiththeadditionofaguidancelawthatmaintainsthefuelratio withinitschokinglimits.Whileadaptivecontrolandguidancecontrolstrategiesfora HSVareinvestigatedin[17,19,41],neitheraddressesthecasewheredynamicsinclude unknownandunmodeleddisturbances.Thereremainsaneedforacontinuouscontroller, whichiscapableofachievingexponentialtrackingforaHSVdynamicmodelcontaining aerothermoelastice ectsandunmodeleddisturbances(i.e.,nonvanishingdisturbancesthat donotsatisfythelinearintheparametersassumption). Inthecontextoftheaforementionedliterature,acontributionofthisdissertation (andinthepublicationsin[51]and[52])isthedevelopmentofacontrollerthatachieves exponentialmodelreferenceoutputtrackingdespiteanuncertainmodeloftheHSV thatincludesnonvanishingexogenousdisturbances.Anonlineartemperature-dependent parameter-varyingstate-spacerepresentationisusedtocapturetheaerothermoelasticeffectsandunmodeleduncertaintiesinaHSV.Thismodelincludesanunknownparametervaryingstatematrix,anuncertainparameter-varyingnon-square(columnde cient)input matrix,andanonlinearadditiveboundeddisturbance.Toachieveanexponentialtracking 14

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resultinlightofthesedisturbances,arobust,continuousLyapunov-basedcontrolleris developedthatincludesanovelimplicitlearningcharacteristicthatcompensatesforthe nonvanishingexogenousdisturbance.Thatis,theuseoftheimplicitlearningmethod enablesthe rstexponentialtrackingresultbyacontinuouscontrollerinthepresenceof theboundednonvanishingexogenousdisturbance.Toillustratetheperformanceofthe developedcontroller,simulationsareperformedonthefullnonlinearmodelgivenin[10] thatincludesaerothermoelasticmodeluncertaintiesandnonlinearexogenousdisturbances whosemagnitudeisbasedonairspeed uctuations. Inadditiontothecontroldevelopment,thereexiststheneedtounderstandthe interplayofacontroldesignwithrespecttothevehicledynamics.Apreviouscontrol orienteddesignanalysisin[6]statesthatsimultaneouslyoptimizingboththestructural dynamicsandcontrolisanintractableproblem,butthatcontrol-orienteddesignmay beperformedbyconsideringtheclosed-loopperformanceofanoptimalcontrollerona seriesofdi erentopen-loopdesignmodels.Thebestperformingdesignmodelisthensaid tohavetheoptimaldynamicsinthesenseofcontrollability.Knowledgeoftheoptimal thermalgradientswillprovideinsighttoengineersonhowtoproperlyweighttheHSVs thermalprotectionsystemforbothsteady-stateandtransient ight.Thepreliminary workbyauthorsin[6]providesacontrol-orienteddesignarchitecturebyinvestigating controlperformancevariationsduetothermalgradientsusingan Hcontroller.Chapter 5seekstoextendthecontrolorienteddesignconcepttoexaminecontrolperformance variationsforHSVmodelsthatincludenonlinearaerothermoelasticdisturbances.Given thesedisturbances,Chapter5focusesonexaminingcontrolperformancevariationsfor themodelreferencerobustcontrollerinChapter2andChapter4toachieveanonlinear control-orientedanalysiswithrespecttothermalgradientsontheHSV.Byanalyzing controlerrorandinputnormsaswellastransientandsteady-stateresponsesoverawide rangeoftemperaturepro lesanoptimaltemperaturepro lerangeissuggested. 15

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1.2OutlineandContributions Thisdissertationfocusesondesigninganonlinearcontrollerforgeneraldisturbed LPVsystem.Thecontrolleristhenmodi edforaspeci cair-breathingHSV.The dynamicinversiondesignisatechniquethatallowsthemultiplicativeinputmatricesto beinverted,thusrenderingthecontrollera neinthecontrol.Previousresultsin[27]and [29]haveexaminedfullstateandoutputfeedbackadaptivedynamicinversioncontrollers, butarelimitedbecausetheycontainconstantuncertainties.TheHSVsystempresents anewchallengebecausetheuncertainstate andinputmatricesareparametervarying. Speci cally,thestateandinputmatricesofthehypersonicvehiclevaryasafunctionof temperature.Thischapterprovidessomebackgroundandmotivatestherobustdynamic inversioncontrolmethodsubsequentlydeveloped.Abriefoutlineofthefollowingchapters follows. InChapter2atrackingcontrollerispresentedthatachievesexponentialstabilityof amodelreferencesysteminthepresenceofuncertaintiesanddisturbances.Speci cally, theplantmodelcontainstime-varyingparametricuncertaintywithdisturbancesthatare boundedandnonvanishing.Thecontributionofthisresultisthatitrepresentsthe rst everdevelopmentofanexponentiallystablecontinuousrobustmodelreferencetracking controllerforanLPVsystemwithanunknownsystemmatrixanduncertaininputmatrix withanadditiveunknownboundeddisturbance.Lyapunovbasedmethodsareusedto proveexponentialstabilityofthesystem. Chapter3providesthenonlineardynamicsandtemperaturemodelofaHSV.The nonlinearandhighlycoupleddynamicequationsarepresented.Theequationsthat de netheaerodynamicandgeneralizedmomentsandforcesareprovidedexplicitlyin previousliterature.Thischapterismeanttoserveasanoverviewofthedynamicsofthe HSV.Inadditiontothe ightandstructuraldynamics,temperaturepro lemodelingis provided.TemperaturevariationsimpacttheHSV ightdynamicsthroughchangesinthe structuraldynamicswhicha ectthemodeshapesandnaturalfrequenciesofthevehicle. 16

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Thepresentedmodelo ersanapproximateapproach,wherebythenaturalfrequencies ofacontinuousbeamaredescribedasafunctionofthemassdistributionofabeam anditssti ness.Figuresandtablesarepresentedtoemphasizetheneedtoincludesuch dynamicsforcontroldesign.Thischapterisdesignedtofamiliarizethereaderwiththe HSVdynamicandtemperaturemodels,sincethesedynamicsareusedthroughoutthis dissertation.ThischapterisaprecursorandintroductiontoChapter4andChapter5. UsingthecontrollerdevelopedinChapter2,thecontributioninChapter4isto illustrateanapplicationtoanair-breathinghypersonicvehiclesystemwithadditive boundeddisturbancesandaerothermoelastice ects,wherethecontrolinputismultiplied byanuncertain,columnde cient,parameter-varyingmatrix.Inadditiontothestability proof,thecontroldesignisalsovalidatedthroughimplementationinafullnonlinear dynamicsimulation.Theexogenousdisturbances(e.g.,windgust,enginevariations,etc.) andtemperaturepro les(aerodynamicdriventhermalheating)aredesignedtoexamine therobustnessofthedevelopedcontroller.Theresultsfromthesimulationillustratethe boundednessofthecontrollerwithfavorabletransientandsteadystatetrackingerrorsand provideevidencethatthecontrolmodelusedfordevelopmentisvalid. ThecontributioninChapter5istoprovideananalysisframeworktoexaminethe nonlinearcontrolperformancebasedonvariationsinthevehicledynamics.Speci cally, thechangesoccurinthestructuraldynamicsviatheirresponsetodi erenttemperature pro les,andhencetheobservedvibrationhasdi erentfrequenciesandshapes.Using aninitialrandomsearchandevolvingalgorithms,approximateoptimalgainsarefound forthecontrollerforeachtemperaturedependantplantmodel.Errors,controle ort, transientandsteady-stateperformanceanalysisisprovided.Theresultsfromthisanalysis showthatthereisatemperaturerangeforoperationoftheHSVthatminimizesagiven costofperformanceversuscontrolauthorit y.Knowledgeofafavorablerangewithregard tocontrolperformanceprovidesdesignersanextratoolwhendevelopingthethermal protectionsystemaswellasthestructuralcharacteristicsoftheHSV. 17

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Chapter6summarizesthecontributionsofthedissertationandpossibleavenuesfor futureworkareprovided.Thebriefcontri butionsoftheLPVcontroller,HSVexample controllerdesignapplication,andtheHSVoptimizationprocedureprovidethebaseofthis dissertation.Afterabriefsummary,someofthedrawbacksofthecurrentcontroldesign arepresentedasdirectionsforfutureresearchwork. 18

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CHAPTER2 LYAPUNOV-BASEDEXPONENTIALTRACKINGCONTROLOFLPVSYSTEMS WITHANUNKNOWNSYSTEMMATRIX,UNCERTAININPUTMATRIXVIA DYNAMICINVERSION 2.1Introduction Linearparametervarying(LPV)systemsha veawiderangeofpracticalengineering applications.Someexamplesincludeseveralmissileautopilotdesignsasin[7,39,43], aturbofanengine[5],andactivesuspensiondesign[18].Traditionally,LPVsystems havebeendevelopedusingagainschedulingcontrolapproach.Gainschedulingisa techniquetodevelopcontrollersfornonlinearsystemusingtraditionallinearcontrol theory.Gainschedulingisatechniquewherethesystemislinearizedaboutcertain operatingconditions.Abouttheseoperatingconditions,constantparametersareassumed andseparatecontrolschemesandgainsarechosen.Morethanadecadeago,Shammaet. al.pointedoutsomeofthepotentialhazardsofgainschedulingin[42].Inparticular,gain schedulingisaanalyticallynon-continuousmethodandstabilityisnotguaranteedwhile switchingfromoneregionoflinearizationtoanother.Infactthetwobiggestdownfallsof gainschedulingcontroldesignisthelinearizationoftheplantmodelsclosetoequilibrium orconstantparametersstatesandtherequirementthattheparametersmustchange slowly.Becausethelinearizationisrequiredtobeclosetosomeoperationcondition orstabilitypoint,manydi erentscheduleshavetobetaken.Andbyrequiringthat parameterschangeslowly,thegainschedulingtechniquesarenotappropriateformany quicklyvaryingsystems. AnotherapproachtoLPVproblemsistheuseoflinearmatrixinequalities(LMIs). InabookonLMIsandtheiruseinsystemandcontroltheoryin[11],Boydet.al.states thatLMIsaremathematicallyconvexoptimizationproblemswithextensionstocontrol theory.Howeverin[11]itispointedoutthatthesetypicallyrequirenumericalsolutions andthereareonlyafewspecialcaseswhereanalyticalsolutionsexist.TheseLPV solutionstypicallyonlyprovidenormbasedsolutions.Themostcommonoftheseisthe 19

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2-normbecauseitallowsforcontinuitywith controlwhenthesystemsbecomelinear time-invariant.Forinstance controlisdevelopedin[14]whichusesLMIstooptimize thesolutionandin[3],theparameterizationofLMIswasinvestigatedinthecontextof controltheory. controlisdevelopedin[14],whichusesLMIstooptimizethesolution andSaifet.al.in[48]showsthatstabilizationsolutionsexistformulti-input-multi-output (MIMO)systemsusingLMIs.ThesedesignsallowforthecontinuoussolutionofLPV systems,howeverknowledgeofthestructureofthesystemmustbeknown,andthe parametersareassumedmeasurableonline.In[25]minimaxcontrollersaredesignedto handleonlyconstantorsmallvariationsintheparameters,wheretheparameterized algebraicRiccatiinequalitiesareconvertedintoequivalentLMIssothattheconvexity canbeexploitedandacontrollerdeveloped.ContinuouscontroldesignforuncertainLPV systemsin[13]isdesignedusingLMIs,howevertheprocedureislimitedtouncertaintiesin thestatematrix,anddoesnotcoveruncertaintiesintheinputmatrix. AnotherapproachuseslinearfractionaltransformationsLFTsinthecontextofLPV controldesignsuchasin[31]andarebasedonsmallgaintheory.Thisapproachcannot handleuncertainparameters.However,byextendingthesolutionin[31]thedesigncan includeuncertainparameterswhicharenotavailabletothecontroller.Thesesolutions are -synthesistypecontrollers,howeverthesolvabilityconditionsarenon-convexand thereforeasolutiontotheproblemisnotguaranteedevenwhenastablecontrollerexists. Severalexamplesofrecursive -typesolutionsaregivenin[2,22,45].Morerecentlyin [26],the -typesolutionshavebeenextendedtoahypersonicaircraftexample,butsu ers thesamenon-convexityproblemastheformerlylisted -typeliterature. RecentresearchonnonlinearinversionoftheinputdynamicsbasedonLyapunov stabilitytheoryprovidesasteppingstonetoLPVdynamicinversion.In[27,28],dynamic inversiontechniquesareusedtodesigncontrollersthatcanadaptivelyandrobustly stabilizeamoregeneralstate-spacesystemthathasbeenconsideredinpreviousworkwith uncertainconstantparametersandadditiveunknownboundeddisturbances.However, 20

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thisworkislimitedtotime-invarientparametersandthereforeisnotapplicabletoLPV systems.Theworkpresentedinthischapterisanextensionoftheworkin[27,28],and providesacontinuousrobustcontrollerthatisabletoexponentiallystabilizeLPVsystems withunknownboundeddisturbances,whenboththestate,inputmatrices,time-varying parameters,anddisturbancesareunknown. 2.2LinearParameterVaryingModel Thedynamicmodelusedforthesubsequentcontroldevelopmentisacombinationof linear-parameter-varying(LPV)systemwithanaddedunmodeleddisturbanceas = ( ( )) + ( ( )) + ( ) (21) = (22) In(2)and(22), ( ) Risthestatevector, ( ( )) R denotesalinear parametervaryingstatematrix, ( ( )) denotesalinearparametervarying inputmatrix, R denotesaknownoutputmatrix, ( ) Rdenotescontrol vector, ( ) representstheunknowntime-dependentparameters, ( ) Rrepresentsa time-dependentunknown,nonlineardisturbance,and ( ) Rrepresentsthemeasured outputvector.Thesubsequentcontroldevelopmentisbasedontheassumptionthat ,meaningthatatleastonecontrolinputisavailableforeachoutputstate.Whenthe systemisoveractuatedinthattherearemorecontrolinputsavailablethanoutputstates, then andtheresultinginputdynamicinversionmatrixwillberowde cient.For thiscase,arightpseudo-inversecanbeusedinconjunctionwithasingularityavoidance law.Forinstance,if R thenthepseudo-inverse += 1andsatis es += where isanidentitymatrixofdimension Thematrices ( ( )) and ( ( )) havethestandardlinearparameter-varyingform ( )= 0+P =1( ( )) (23) ( )= 0+P =1( ( )) (24) 21

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where 0 R and 0 R representknownnominalmatriceswithunknown variations ( ( )) and ( ( )) for =1 2 ,where R and R aretime-invariantmatrices,and ( ( )) ( ( )) R areparameter-dependent weightingterms.Knowledgeofthenominalmatrix 0willbeexploitedinthesubsequent controldesign. Tofacilitatethesubsequentcontroldesign,areferencemodelisgivenas = + (25) = (26) where R and R denotethestateandinputmatrices,respectively,where isHurwitz, ( ) Risavectorofreferenceinputs, ( ) Rarethereference outputs,and wasde nedin(22). Assumption1:Thenonlineardisturbance ( ) andits rsttwotimederivativesare assumedtoexistandbeboundedbyknownconstants. Assumption2:Thedynamicsin(2)areassumedtobecontrollable. Assumption3:Thematrices ( ( )) and ( ( )) andtheirtimederivativessatisfy thefollowinginequalities: k ( ( )) k k ( ( ))k (27) ( ( )) ( ( )) where R+areknownboundingconstants,and kkdenotestheinduced in nitynormofamatrix.Asistypicalinrobustcontrolmethods,knowledgeoftheupper boundsin(27)areusedtodevelopsu cientconditionsongainsusedinthesubsequent controldesign. 22

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2.3ControlDevelopment 2.3.1ControlObjective Thecontrolobjectiveistoensurethattheoutput ( ) tracksthetime-varyingoutput generatedfromthereferencemodelin(2)and(2).Toquantifythecontrolobjective, anoutputtrackingerror,denotedby ( ) R,isde nedas = ( ) (28) Tofacilitatethesubsequentanalysis,a lteredtrackingerrordenotedby ( ) R,is de nedas + (29) where R2isapositivede nitediagonal,constantcontrolgainmatrix,andisselectedtoplacearelativeweightontheerrorst ateversesitsderivative.Tofacilitatethe subsequentrobustcontroldevelopment,thestatevector ( ) isexpressedas ( )= ( )+ ( ) (2) where ( ) Rcontainsthe outputstates,and ( ) Rcontainstheremaining states.Likewise,thereferencestates ( ) canalsobeseparatedasin(2). Assumption4:Thestatescontainedin ( ) in(2)andthecorrespondingtime derivativescanbefurtherseparatedas ( )= ( )+ ( ) (2) ( )= ( )+ ( ) where ( ) ( ) ( ) ( ) Rareupperboundedas k ( ) k 1k kk ( ) k (2) k ( ) k 2k kk ( ) k 23

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where ( ) R2 isde nedas (2) and 12 R areknownnon-negativeboundingconstants.Thetermsin(2) and(2)areusedtodevelopsu cientgainconditionsforthesubsequentrobustcontrol design. 2.3.2Open-LoopErrorSystem Theopen-looptrackingerrordynamicscanbedevelopedbytakingthetimederivativeof(29)andusingtheexpressionsin(21)-(2)as = + = ( )+ = + + + + ( ) + = + + + (2) Theauxiliaryfunctions ( ) Rand Rin(2) arede nedas ( )+ ( )+ + + + (2) and ( )+ + + + (2) Motivationfortheselectivegroupingofthetermsin(2)and(2)isderivedfromthe factthatthefollowinginequalitiescanbedeveloped[38,54]as 0k kk k (2) where 0 R+areknownboundingconstants. 24

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2.3.3Closed-LoopErrorSystem Basedontheexpressionin(24)andthesubsequentstabilityanalysis,thecontrol inputisdesignedas = ( 0) 1[( + ) ( ) ( + ) (0)+ ( )] (2) where ( ) Risanimplicitlearninglawwithanupdaterulegivenby ( )= k ( ) k ( ( ))+( + ) ( )+ ( ( )) (2) and R R denotepositivede nite,diagonalconstantcontrolgain matrices, 0 R isintroducedin(24), ( ) denotesthestandardsignumfunction wherethefunctionisappliedtoeachelementofthevectorargument,and denotesa identitymatrix. Aftersubstitutingthetimederivativeof(2)into(2),theerrordynamicscanbe expressedas = + k ( ) k ( ( ))+ (2) ( + ) ( ) ( ( )) wheretheauxiliarymatrix ( ( )) R isde nedas ( 0) 1(2) where ( ( )) canbeseparatedintodiagonal(i.e., ( ( )) R )ando -diagonal(i.e., ( ( )) R )componentsas = + (2) Assumption5:Thesubsequentdevelopmentisbasedontheassumptionthatthe uncertainmatrix ( ( )) isdiagonallydominantinthesensethat min( ) k k (2) 25

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where R+isaknownconstant.Whilethisassumptioncannotbevalidatedfora genericsystem,theconditioncanbechecked(withinsomecertaintytolerances)fora speci csystem.Essentially,thisconditionindicatesthatthenominalvalue 0must remainwithinsomeboundedregionof .Inpractice,boundsonthevariationof should beknown,foraparticularsystemunderasetofoperatingconditions,andthisboundcan beusedtocheckthesu cientconditionsgivenin(2). Motivationforthestructureofthecontrollerin(2)and(2)comesfromthe desiretodevelopaclosed-looperrorsystemtofacilitatethesubsequentLyapunov-based stabilityanalysis.Inparticular,sincethecontrolinputispremultipliedbytheuncertain matrix in(2),theterm 1 0ismotivatedtogeneratetherelationshipin(21) sothatifthediagonaldominanceassumption(Assumption5)issatis ed,thenthecontrol canprovidefeedbacktocompensateforthedisturbanceterms.Thebracketedtermsin (28)includethestatefeedback,aninitialconditionterm,andtheimplicitlearningterm. Theimplicitlearningterm ( ) isthegeneralizedsolutionto(29).Thestructureofthe updatelawin(219)ismotivatedbytheneedtorejecttheexogenousdisturbanceterms. Speci cally,theupdatelawismotivatedbyaslidingmodecontrolstrategythatcanbe usedtoeliminateadditiveboundeddisturbances.Unlikeslidingmodecontrol(which isadiscontinuouscontrolmethodrequiringin niteactuatorbandwidth),thecurrent continuouscontrolapproachincludestheintegralofthe ( ) function.Thisimplicit learninglawisthekeyelementthatallowsthecontrollertoobtainanexponentialstability resultdespitetheadditivenonvanishingexog enousdisturbance.Otherresultsinliterature alsohaveusedtheimplicitlearningstructureinclude[33,34,35,36,37,40]. 26

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Di erentialequationssuchas(224)and(225)havediscontinuousright-handsides as ( )= k ( ) k ( ( ))+(+ ) ( )+ ( ( )) (2) = + k ( ) k ( ( ))+ ( + ) ( ) ( ( )) (2) Let ( ) R2 denotetheright-hands ideof(224)and(225).Sincethesubsequent analysisrequiresthatasolutionexistfor = ( ) ,itisimportanttoshowthe existenceofthegeneralizedsolution.TheexistenceofFilippovsgeneralizedsolution [15]canbeestablishedfor(2)and(2).First,notethat ( ) iscontinuous exceptintheset { ( ) | =0 } .Let ( ) beacompact,convex,uppersemicontinuous set-valuedmapthatembedsthedi erentialequation = ( ) intothedi erential inclusion ( ) .Anabsolutecontinuoussolutionexiststo = ( ) thatisa generalizedsolutionto = ( ) .Acommonchoice[15]for ( ) thatsatis esthe aboveconditionsistheclosedconvexhullof ( ) .Aproofthatthischoicefor ( ) isuppersemicontinuousisgivenin[20]. 2.4StabilityAnalysis Theorem: Thecontrollergivenin(2)and(2)ensuresexponentialtrackingin thesensethat k ( ) k k (0) k exp 1 2 [0 ) (2) where 1 R+,providedthecontrolgains ,and introducedin(28)areselected accordingtothesu cientconditions min( ) min( ) 2 0 4 min { } min( ) (2) where 0and areintroducedin(2), isintroducedin(223), R+isa knownpositiveconstant,and min( ) denotestheminimumeigenvalueoftheargument. Theboundingconstantsareconservativeupperboundsonthemaximumexpected 27

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values.TheLyapunovanalysisindicatesthatthegainsin(227)needtobeselected su cientlylargebasedonthebounds.Therefore,iftheconstantsarechosentobe conservative,thenthesu cientgainconditionswillbelarger.Valuesforthesegainscould bedeterminedthroughaphysicalunderstandingofthesystem(withinsomeconservative %ofuncertainty)and/orthroughnumericalsimulations. Proof :Let ( ): R2 [0 ) R beaLipschitzcontinuous,positivede nite functionde nedas ( ) 1 2 + 1 2 (2) where ( ) and ( ) arede nedin(2)and(2),respectively.Aftertakingthetime derivativeof(2)andutilizing(2),(2),and(2), ( ) canbeexpressedas ( )= + + ( + ) ( + ) (2) k k ( ) k k ( ) ( ) ( )+ Byutilizingtheboundingargumentsin(217)andAssumptions3and5,theupperbound oftheexpressionin(229)canbeexplicitlydetermined.Speci cally,basedon(27)of Assumption3,theterm in(2)canbeupperboundedas k kk k (2) Afterutilizinginequality(23)ofAssumption5,thefollowinginequalitiescanbe developed: ( + ) ( + ) ( min( )+1) k k2 k ( ) k ( ) k ( ) k ( ) min( ) | |k k (2) ( ) ( ) min( ) | | 28

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Afterusingtheinequalitiesin(230)and(231),theexpressionin(229)canbeupper boundedas ( ) k k2+ + k kk k ( min( )+1) k k2(2) min( ) k kk k min( ) k k + wherethefactthat | | k k Rwasutilized.Afterutilizingtheinequalitiesin (27)andrearrangingtheresultingexpression,theupperboundfor ( ) canbe expressedas ( ) k k2 k k2 min( ) k k2+ 0k kk k (2) [ min( ) ] k kk k [ min( ) ] k k If and satisfythesu cientgainconditionsin(227),thebracketedtermsin(23) arepositive,and ( ) canbeupperboundedusingthesquaresofthecomponentsof ( ) as: ( ) k k2 k k2 min( ) k k2 0k kk k (2) Bycompletingthesquares,theupperboundin(234)canbeexpressedinamore convenientform.Tothisend,theterm2 0k k2 4 min( )isaddedandsubtractedtotherighthand sideof(2)yielding ( ) k k2 k k2 min( ) k k 0k k 2 min( ) 2+ 2 0k k2 4 min( ) (2) Sincethesquareofthebracketedtermin(25)isalwayspositive,theupperboundcan beexpressedas ( ) { } + 2 0k k2 4 min( ) (2) where ( ) isde nedin(2).Hence,(2)canbeusedtorewritetheupperboundof ( ) as ( ) min { } 2 0 4 min( ) k k2 (2) 29

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wherethefactthat { } min { }k k2wasutilized.Provided thegainconditionin(2)issatis ed,(228)and(237)canbeusedtoshowthat ( ) ;hence ( ) ( ) .Giventhat ( ) ( ) ,standardlinearanalysis methodscanbeusedtoprovethat ( ) from(2).Since ( ) ( ) ,the assumptionthatthereferencemodeloutputs ( ) ( ) canbeusedalongwith (2)toprovethat ( ) ( ) .Giventhat ( ) ( ) ( ) ( ) ,thevector ( ) ,thetimederivative ( ) ,and(2)-(2)canbeusedtoshowthat ( ) ( ) .Giventhat ( ) ( ) ,Assumptions1,2,and3canbeutilized alongwith(2)toshowthat ( ) Thede nitionfor ( ) in(2)canbeusedalongwithinequality(2)toshow that ( ) canbeupperboundedas ( ) 1( ) (2) providedthesu cientconditionin(27)issatis ed.Thedi erentialinequalityin(238) canbesolvedas ( ) ( (0) 0)exp( 1 ) (2) Hence,(2),(2),and(2)canbeusedtoconcludethat k ( ) k k (0) k exp 1 2 [0 ) (2) 2.5Conclusions AcontinuousexponentiallystablecontrollerwasdevelopedforLPVsystemswithan unknownstatematrix,anuncertaininputmatrix,andanunknownadditivedisturbance. ThisworkpresentsanewapproachtoLPVcontrolbyinvertingtheuncertaininput dynamicsandrobustlycompensatingforotherunknownsanddisturbances.Thecontroller isvalidforLPVsystemswherethereareatleastasmanycontrolinputsasthereare outputs.UsingthistechniqueitispossiblecontrolLPVsystemswherethereisahigh amountofuncertaintyandnonlinearitiesthatinvalidatetraditionalLPVapproaches. 30

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Robustdynamicinversioncontrolispossibleforawiderangeofpracticalsystemsthatare approximatedasanLPVsystemwithadditivedisturbances.Futureworkwillfocuson relaxingtheassumptionswhilemaintainingthestabilityandperformance. 31

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CHAPTER3 HYPERSONICVEHICLEDYNAMICSANDTEMPERATUREMODEL 3.1Introduction Inthischapterthedynamicsofthehypersonicvehicle(HSV)areintroduced,includingboththestandard ightdynamicsandthestructuralvibrationdynamics.After thedynamicsaredevelopedandthe ightandstructuralcomponentsareexplained,a temperaturemodelisintroduced.Becausechangesintemperaturechangethestructural dynamics,coupledforcingtermschangethethe ightdynamics.Examplesoflineartemperaturepro lesareprovided,andsomeexamplesofthestructuralmodesandfrequencies areexplained. 3.2RigidBodyandElasticDynamics Toincorporatestructuraldynamicsandaerothermoelastice ectsintheHSVdynamic model,anassumedmodesmodelisconsideredforthelongitudinaldynamics[53]as = cos( ) sin( ) (31) = sin( ) (32) = + sin( ) + + cos( ) (33) = (34) = (35) = 2 2 + =1 2 3 (36) In(3)-(3), ( ) denotestheforwardvelocity, ( ) denotesthealtitude, ( ) denotestheangleofattack, ( ) denotesthepitchangle, ( ) ispitch rate,and ( ) =1 2 3 denotesthe generalizedstructuralmodedisplacement. Alsoin(31)-(3), denotesthevehiclemass, isthemomentofinertia, istheaccelerationduetogravity, ( ) ( ) arethedampingfactorand naturalfrequencyofthe exiblemode,respectively, ( ) denotesthethrust, 32

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( ) denotesthedrag, ( ) isthelift, ( ) isthepitchingmomentabout thebody -axis,and ( ) =1 2 3 denotesthegeneralizedelasticforces,where ( ) 11iscomposedofthe 5 ightand 6 structuraldynamicstatesas = 1 12 23 3 (37) Theequationsthatde netheaerodynamicandgeneralizedmomentsandforcesare highlycoupledandareprovidedexplicitlyinpreviouswork[10].Speci cally,therigid bodyandelasticmodesarecoupledinthesensethat ( ) ( ) ( ) ,arefunctions of ( ) andthat ( ) isafunctionoftheotherstates.Asthetemperaturepro le changes,themodulusofelasticityofthevehiclechangesandthedampingfactorsand naturalfrequenciesofthe exiblemodeswillchange.Thesubsequentdevelopmentexploits animplicitlearningcontrolstructure,designedbasedonanLPVapproximationofthe dynamicsin(31)-(36),toyieldexponentialtrackingdespitetheuncertaintyduetothe unknownaerothermoelastice ectsandadditionalunmodeleddynamics. 3.3TemperaturePro leModel TemperaturevariationsimpacttheHSV ightdynamicsthroughchangesinthe structuraldynamicswhicha ectthemodeshapesandnaturalfrequenciesofthevehicle. Thetemperaturemodelusedassumesafree-freebeam[10],whichmaynotcapturethe actualaircraftdynamicsproperly.Inreali ty,theinternalstructurewillbemadeofa complexnetworkofstructuralelementsthatwillexpandatdi erentratescausingthermal stresses.Thermalstressesa ectdi erentmodesindi erentmanners,whereitraises thefrequenciesofsomemodesandlowersothers(comparedtoauniformdegradation withYoungsmodulusonly).Therefore,thecurrentmodelonlyo ersanapproximate approach.Thenaturalfrequenciesofacontinuousbeamareafunctionofthemass distributionofthebeamandthesti ness.Inturn,thesti nessisafunctionofYoungs Modulus(E)andadmissiblemodefunctions.Hence,bymodelingYoungsModulusasa functionoftemperature,thee ectoftemperatureon ightdynamicscanbecaptured. 33

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Thermostructuraldynamicsarecalculatedunderthematerialassumptionthattitanium isbelowthethermalprotectionsystem[9,12].YoungsModulus(E)andthenatural dynamicfrequenciesforthe rstthreemodesofatitaniumfree-freebeamaredepictedin Figure3-1andFigure3-2respectively. 0 100 200 300 400 500 600 700 800 900 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 Temperature (F)E (Modulus of Elasticity in psi)Figure3-1:Modulusofelasticityforthe rstthreedynamicmodesofvibrationforafreefreebeamoftitanium. InFigure3-1,themoduliforthethreemodesarenearlyidentical.Thetemperature rangeshowncorrespondstothetemperaturerangethatwillbeusedinthesimulation section.FrequenciesinFigure3-2correspondtoasolidtitaniumbeam,whichwillnot correspondtotheactualnaturalfrequenciesoftheaircraft.ThedatashowninFigure3-1 andFigure3-2arebothfrompreviousexperimentalwork[47].Usingthisdata,di erent temperaturegradientsalongthefuselageareintroducedintothemodelanda ectthe structuralpropertiesoftheHSV.ThesimulationsinChapter4andChapter5uselinearly decreasinggradientsfromthenosetothetailsection.Itsexpectedthatthenosewill bethehottestpartofthestructureduetoaerodynamicheatingbehindthebowshock wave.Thermostructuraldynamicsarecalculatedundertheassumptionthattherearenine constant-temperaturesectionsintheaircraft[6]asshowninFigure3-3.Sincetheaircraft is100feetlong,thelengthofeachoftheninesectionsisapproximately11.1feet. 34

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0 100 200 300 400 500 600 700 800 900 45 50 55 1st Dynamic ModeFrequency (Hz) 0 100 200 300 400 500 600 700 800 900 120 140 160 2nd Dynamic ModeFrequency (Hz) 0 100 200 300 400 500 600 700 800 900 200 250 300 3rd Dynamic Mode Temperature (F)Frequency (Hz)Figure3-2:Frequenciesofvibrationforthe rstthreedynamicmodesofafree-freetitaniumbeam. Figure3-3:NineconstanttemperaturesectionsoftheHSVusedfortemperaturepro le modeling. 35

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Table3-1:Naturalfrequenciesfor5lineartemperaturepro les(Nose/Tail)indegrees F.Percentdi erenceisthedi erencebetweenthemaximumandminimumfrequencies dividedbytheminimumfrequency. Mode 900 500800 400700 300600 200500 100 %Di erence 1(Hz)23.023.523.924.324.77.39 % 2(Hz)49.950.951.852.653.57.21 % 3(Hz)98.9101.0102.7104.4106.27.38 % Thestructuralmodesandfrequenciesarecalculatedusinganassumedmodesmethod with niteelementdiscretization,includingvehiclemassdistributionandinertiae ects. Theresultofthismethodisthegeneralizedmodeshapesandmodefrequenciesforthe HSV.Becausethebeamisnon-uniformintemperature,themodulusofelasticityisalso non-uniform,whichproducesasymmetricmodeshapes.Anexampleoftheasymmetric modeshapesisshowninFigure3-5andtheasymmetryisduetovariationsin resulting fromthefactthateachoftheninefuselagesections(seeFigure3-3)hasadi erent temperatureandhencedi erent exibledynamicproperties.Anexampleofsomeof themodefrequenciesareprovidedinTable 1 ,whichshowsthevariationinthenatural frequenciesfor vedecreasinglineartemperaturepro lesshowninFigure3-4.Forall threenaturalmodes,Table3-1showsthatthenaturalfrequencyforthe rsttemperature pro leisalmost 7% lowerthanthatofthe fthtemperaturepro le. Thetemperaturepro leinaHSVisacomplexfunctionofthestatehistory,structuralproperties,thermalprotectionsystem,etc.ForthesimulationsinChapter4and Chapter5,thetemperaturepro leisassumedtobealinearfunctionthatdecreasesfrom thenosetothetailoftheaircraft.Thelinearpro lesarethenvariedtospanapreselecteddesignspace.Ratherthanattemptingtomodelaphysicaltemperaturegradientfor somevehicledesign,thetemperaturepro leinthesimulationsinChapter4andChapter5isintendedtoprovideanaggressivetemperaturedependentpro letoexaminethe robustnessofthecontrollertosuch uctuations. 36

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1 2 3 4 5 6 7 8 9 100 200 300 400 500 600 700 800 900 Fuselage sectionTemperature (F)Figure3-4:Lineartemperaturepro lesusedtocalculatevaluesshowninTable3-1. 0 20 40 60 80 100 .4 .3 .2 .1 0 0.1 0.2 0.3 Fuselage Position (ft)Displacement 1st 2nd 3rd Figure3-5:Asymetricmodeshapesforthehypersonicvehicle.Thepercentdi erencewas calculatedbasedonthemaximumminustheminimumstructuralfrequenciesdividedby theminimum. 37

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3.4Conclusion Thischapterexplainstheoverall ightandstructuraldynamicsforaHSV,inthe presenceofdi erenttemperaturepro les.Thesedynamicsareimportanttounderstand becausechangesinthetemperaturepro lemodifythedynamics,hencecanbemodeled asadditiveparameterdisturbances.Inthef ollowingchapters,theHSVdynamicswillbe reducedtoaLPVsystemwithanadditivedisturbance,andthecontrollerfromChapter 2willbeapplied.Thetemperaturepro leswillactastheparametervariations.This chapterwasmeanttobrie yintroducetheoverallsystemandexplainthestructural modes,shapes,andfrequencies.Datawasshowntomotivatethefactthatchangesin temperaturesubstantiallya ecttheoveralldynamics. 38

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CHAPTER4 LYAPUNOV-BASEDEXPONENTIALTRACKINGCONTROLOFAHYPERSONIC AIRCRAFTWITHAEROTHERMOELASTICEFFECTS 4.1Introduction Thedesignofguidanceandcontrolsystemsforairbreathinghypersonicvehicles (HSV)ischallengingbecausethedynamicsoftheHSVarecomplexandhighlycoupled [10],andtemperature-inducedsti nessvariationsimpactthestructuraldynamics[21]. Thestructuraldynamics,inturn,a ecttheaerodynamicproperties.Vibrationinthe forwardfuselagechangestheapparentturnangleofthe ow,whichresultsinchanges inthepressuredistributionovertheforebodyoftheaircraft.Theresultingchangesin thepressuredistributionovertheaircraftmanifestthemselvesasthrust,lift,drag,and pitchingmomentperturbations[10].Todevelopcontrollawsforthelongitudinaldynamics ofaHSVcapableofcompensatingforthesestructuralandaerothermoelastice ects, structuraltemperaturevariationsandstructuraldynamicsmustbeconsidered. Aerothermoelasticityistheresponseofelasticstructurestoaerodynamicheatingand loading.Aerothermoelastice ectscannotbeignoredinhypersonic ight,becausesuch e ectscandestabilizetheHSVsystem[21].Alossofsti nessinducedbyaerodynamic heatinghasbeenshowntopotentiallyinducedynamicinstabilityinsupersonic/hypersonic ightspeedregimes[1].Yetactivecontrolcanbeusedtoexpandthe utterboundary andconvertunstablelimitcycleoscillations(LCO)tostableLCO[1].Anactivestructural controllerwasdeveloped[26],whichaccountsforvariationsintheHSVstructuralpropertiesresultingfromaerothermoelastice ects.Thecontroldesign[26]modelsthestructural dynamicsusingaLPVframework,andstatesthebene tstousingtheLPVframework aretwo-fold:thedynamicscanberepresentedasasinglemodel,andcontrollerscanbe designedthathavea nedependencyontheoperatingparameters. Previouspublicationshaveexaminedthechallengesassociatedwiththecontrolof HSVs.Forexample,HSV ightcontrollersaredesignedusinggeneticalgorithmstosearch adesignparameterspacewherethenonlinearlongitudinalequationsofmotioncontain 39

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uncertainparameters[4,30,49].SomeofthesedesignsutilizeMonteCarlosimulations toestimatesystemrobustnessateachsearchiteration.Anotherapproach[4]istouse fuzzylogictocontroltheattitudeoftheHSVaboutasinglelowend ightcondition. Whilesuchapproaches[4,30,49]generatestabilizingcontrollers,theproceduresare computationallydemandingandrequiremultipleevaluationsimulationsoftheobjective functionandhavelargeconvergenttimes.An adaptivegain-scheduledcontroller[55]was designedusingestimatesofthescheduledparameters,andasemi-optimalcontrolleris developedtoadaptivelyattain controlperformance.Thiscontrolleryieldsuniformly boundedstabilityduetothee ectsofapproximationerrorsandalgorithmicerrorsin theneuralnetworks.FeedbacklinearizationtechniqueshavebeenappliedtoacontrolorientedHSVmodeltodesignanonlinearcontroller[32].Themodel[32]isbasedon apreviouslydeveloped[8]HSVlongitudinaldynamicmodel.Thecontroldesign[32] neglectsvariationsinthrustliftparameters,altitude,anddynamicpressure.Linearoutput feedbacktrackingcontrolmethodshavebeendeveloped[44],wheresensorplacement strategiescanbeusedtoincreaseobservability,orreconstructfullstateinformation forastate-feedbackcontroller.Arobustoutputfeedbacktechniqueisalsodeveloped forthelinearparameterizableHSVmodel,whichdoesnotrelyonstateobservation.A robustsetpointregulationcontroller[17]isdesignedtoyieldasymptoticregulationinthe presenceofparametricandstructuraluncertaintyinalinearparameterizableHSVsystem. Anadaptivecontroller[19]wasdesignedto handle(linearintheparameters)modelinguncertainties,actuatorfailures,andnon-minimumphasedynamics[17]foraHSV withelevatorandfuelratioinputs.Anotheradaptiveapproach[41]wasrecentlydevelopedwiththeadditionofaguidancelawthatmaintainsthefuelratiowithinitschoking limits.WhileadaptivecontrolandguidancecontrolstrategiesforaHSVareinvestigated [17,19,41],neitheraddressesthecasewheredynamicsincludeunknownandunmodeled disturbances.Thereremainsaneedforacontinuouscontroller,whichiscapableofachievingexponentialtrackingforaHSVdynamicmodelcontainingaerothermoelastice ects 40

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andunmodeleddisturbances(i.e .,nonvanishingdisturbances thatdonotsatisfythelinear intheparametersassumption). Inthecontextoftheaforementionedliterature,thecontributionofthecurrenteffort(andthepreliminarye ortbytheauthors[52])isthedevelopmentofacontroller thatachievesexponentialmodelreferenceoutputtrackingdespiteanuncertainmodelof theHSVthatincludesnonvanishingexogenousdisturbances.Anonlineartemperaturedependentparameter-varyingstate-spacerepresentationisusedtocapturetheaerothermoelastice ectsandunmodeleduncertaintiesinaHSV.Thismodelincludesanunknown parameter-varyingstatematrix,anuncertainparameter-varyingnon-square(column de cient)inputmatrix,andanonlinearaddit iveboundeddisturbance.Toachievean exponentialtrackingresultinlightofthesedisturbances,arobust,continuousLyapunovbasedcontrollerisdevelopedthatincludesanovelimplicitlearningcharacteristicthat compensatesforthenonvanishingexogenous disturbance.Thatis,theuseoftheimplicit learningmethodenablesthe rstexponentialtrackingresultbyacontinuouscontrollerin thepresenceoftheboundednonvanishingexogenousdisturbance.Toillustratetheperformanceofthedevelopedcontrollerduringvelocity,angleofattack,andpitchratetracking, simulationsforthefullnonlinearmodel[10]areprovidedthatincludeaerothermoelastic modeluncertaintiesandnonlinearexogenousdisturbanceswhosemagnitudeisbasedon airspeed uctuations. 4.2HSVModel Thedynamicmodelusedforthesubsequentcontroldesignisbasedonareduction ofthedynamicsin(3)-(3)tothefollowing combinationoflinear-parameter-varying (LPV)statematricesandadditivedisturbancesarisingfromunmodelede ectsas = ( ( )) + ( ( )) + ( ) (41) = (42) 41

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In(4)and(42), ( ) R11isthestatevector, ( ( )) R11 11denotesalinear parametervaryingstatematrix, ( ( )) 11 3denotesalinearparametervaryinginput matrix, R3 11denotesaknownoutputmatrix, ( ) R3denotesavectorof 3 control inputs, ( ) representstheunknowntime-dependentparameters, ( ) R11representsa time-dependentunknown,nonlineardisturbance,and ( ) R3representsthemeasured outputvectorofsize 3 4.3ControlObjective Thecontrolobjectiveistoensurethattheoutput ( ) tracksthetime-varyingoutput generatedfromthereferencemodellikesta tedinChapter2.Toquantifythecontrol objective,anoutputtrackingerror,denotedby ( ) R3,isde nedas = ( ) (43) Tofacilitatethesubsequentanalysis,a lteredtrackingerrordenotedby ( ) R3,is de nedas + (44) where R3isapositivede nitediagonal,constantcontrolgainmatrix,andisselected toplacearelativeweightontheerrorstateversesitsderivative.Basedonthecontrol designpresentedinChapter2thecontrolinputisdesignedas = ( 0) 1[( + 3 3) ( ) ( + 3 3) (0)+ ( )] (45) where ( ) R3isanimplicitlearninglawwithanupdaterulegivenby ( )= k ( ) k ( ( ))+(+ 3 3) ( )+ ( ( )) (46) and R3 3denotepositivede nite,diagonalconstantcontrolgainmatrices, 0 R11 3representsaknownnominalinputmatrix, ( ) denotesthestandard signumfunctionwherethefunctionisappliedtoeachelementofthevectorargument, and 3 3denotesa 3 3 identitymatrix.Toillustratetheperformanceofthecontroller 42

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andpracticalityoftheassumptions,anumericalsimulationwasperformedonthefull nonlinearlongitudinalequationsofmotion[10]givenin(3)-(36).Thecontrolinputs wereselectedas = ( ) ( ) ( ) asinpreviousresearch[41],where ( ) and ( ) denotetheelevatorandcanardde ectionangles,respectively, ( ) isthefuel equivalenceratio.Thedi userarearatioisleftatitsoperationaltrimconditionwithout lossofgenerality( ( )=1 ).Thereferenceoutputswereselectedasmaneuveroriented outputsofvelocity,angleofattack,andpitchrateas = ( ) ( ) ( ) where theoutputandstatevariablesareintroducedin(3)-(3).Inaddition,theproposed controllercouldbeusedtocontrolotheroutputstatessuchasaltitudeprovidedthe followingconditionisvalid.Theauxiliarymatrix ( ( )) R isde nedas ( 0) 1(47) where ( ( )) canbeseparatedintodiagonal(i.e., ( ( )) R )ando -diagonal(i.e., ( ( )) R )componentsas = + (48) Theuncertainmatrix ( ( )) isdiagonallydominantinthesensethat min( ) k k (49) where R+isaknownconstant.Whilethisassumptioncannotbevalidatedfora genericHSV,theconditioncanbechecked(withinsomecertaintytolerances)foragiven aircraft.Essentially,thisconditionindicatesthatthenominalvalue 0mustremain withinsomeboundedregionof .Inpractice,bandsonthevariationof shouldbe known,foraparticularaircraftunderasetofoperatingconditions,andthisbandcould beusedtocheckthesu cientconditions.Forthespeci cHSVexamplethisChapter simulates,theassumtionin49isvalid. 43

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4.4SimulationResults TheHSVparametersusedinthesimulationare =75 000 = 86723 2,and =32 174 2 asde nedin(3)-(3).Thesimulationwasexecutedfor 35 secondstosu cientlycyclethroughthedi erenttemperaturepro les.Othervehicle parametersinthesimulationarefunctionsofthetemperaturepro le.Lineartemperature pro lesbetweentheforebody(i.e., [450 900] )andaftbody(i.e., [100 800] ) wereusedtogenerateelasticmodeshapesandfrequenciesbyvaryingthelineargradients as ( )=675+225cos 10 ( )= 450+350cos 3 if ( ) ( ) ( ) otherwise. (4) Figure4-1showsthetemperaturevariationa safunctionoftime.Theirregularitiesseen intheaftbodytemperaturesoccurbecausethetemperaturepro leswereadjustedto ensurethetailoftheaircraftwasequalorco olerthanthenoseoftheaircraftaccording tobowshockwavethermodynamics.Whiletheshockwavethermodynamicsmotivated theneedtoonlyconsiderthecasewhenthetailoftheaircraftwasequalorcoolerthan thenoseoftheaircraft,theshapeofthetemperaturepro leisnotphysicallymotivated. Speci cally,thefrequenciesofoscillationin(410)wereselectedtoaggressivelyspanthe availabletemperatureranges.Thesetemperaturepro lesarenotmotivatedbyphysical temperaturegradients,butmotivatedbythedesiretogenerateatemperaturedisturbance toillustratethecontrollerrobustnesstothetemperaturegradients.Thesimulation assumesthedampingcoe cientremainsconstantforthestructuralmodes ( =0 02) Inadditiontothermoelasticity,aboundednonlineardisturbancewasaddedtothe dynamicsas = 000 10 20 3 (4) 44

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0 5 10 15 20 25 30 3 5 0 200 400 600 800 1000 Nose Temperature (F)Time (s) 0 5 10 15 20 25 30 3 5 0 200 400 600 800 Time (s)Tail Temperature (F)Figure4-1:Temperaturevariationfortheforebodyandaftbodyofthehypersonicvehicle asafunctionoftime. where ( ) R denotesalongitudinalaccelerationdisturbance, ( ) R denotesaangle ofattackrateofchangedisturbance, ( ) R denotesanangularaccelerationdisturbance,and 1( ) 2( ) 3( ) R denotestructuralmodeaccelerationdisturbances.The disturbancesin(4)weregeneratedasana rbitraryexogenousinput(i.e.,unmodeled nonvanishingdisturbancethatdoesnotsatis fythelinearintheparametersassumption) asdepictedinFigure4-2.However,themagni tudesofthedisturbancesweremotivatedby thescenarioofa 300 changeinairspeed.Thedisturbancesarenotdesignedtomimic theexacte ectsofawindgust,buttodemonstratetheproposedcontrollersrobustness withrespecttorealisticallyscaleddisturbances.Speci cally,arelativeforcedisturbanceis determinedbycomparingthedragforce atMach 8 at 85 000 (i.e., 7355 )with thedragforceafteraddinga 300 (e.g.,awindgust)disturbance.UsingNewtons secondlawanddividingthedragforcedi erential bythemassoftheHSV ,a realisticupperboundforan accelerationdisturbance ( ) wasdetermined.Similarly,the sameprocedurecanbeperformed,tocomparethechangeinpitchingmoment caused bya 300 headwindgust.Bydividingthemomentdi erentialbythemomentof 45

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0 5 10 15 20 25 30 3 5 0 1 x 10 f1 (ft/s2) 0 5 10 15 20 25 30 3 5 0 10 f2 (deg/s) 0 5 10 15 20 25 30 3 5 0 2 f3 (deg/s2) 0 5 10 15 20 25 30 3 5 .05 0 0.05 f7 (1/s2) 0 5 10 15 20 25 30 3 5 .01 0 0.01 f9 (1/s2) 0 5 10 15 20 25 30 3 5 0 1 x 10 f11 (1/s2)Time (s)Figure4-2:Inthis gure, denotesthe elementinthedisturbancevecor .Disturbancesfromtoptobottom:velocity ,angleofattack ,pitchrate ,the 1elastic structuralmode 1,the 2elasticstructuralmode 2,andthe 3elasticstructuralmode 3,asdescribedin(411). inertiaoftheHSV ,arealisticupperboundfor ( ) canbedetermined.Tocalculate areasonableangleofattackdisturbancemagnitude,averticalwindgustof 300 is considered.Bytakingtheinversetangentoftheverticalwindgustdividedbytheforward velocityatMach 8 and 85 000 ,anupperboundfortheangleofattackdisturbance ( ) canbedetermined.Disturbancesforthestructuralmodes ( ) wereplacedonthe accelerationtermswith ( ) ,whereeachsubsequentmodeisreducedbyafactorof10 relativetothe rstmode,seeFigure4-2. Theproposedcontrollerisdesignedtofollowtheoutputsofawellbehavedreference model.Toobtaintheseoutputs,areferencemodelthatexhibitedfavorablecharacteristics wasdesignedfromastaticlinearizeddynamicsmodelofthefullnonlineardynamics [10].Thereferencemodeloutputsareshowni nFigure4-3.Thevelocityreferenceoutput followsa 1000 smoothstepinput,whilethepitchrateperformsseveral 1 maneuvers.Theangleofattackstayswithin 2 degrees. 46

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0 5 10 15 20 25 30 3 5 7000 7500 8000 8500 Vm (ft/s) 0 5 10 15 20 25 30 3 5 0 2 m (deg) 0 5 10 15 20 25 30 3 5 0 1 2 Qm (deg/s)Time (s)Figure4-3:Referencemodelouputs ,whicharethedesiredtrajectoriesfortop:velocity ( ) ,middle:angleofattack ( ) ,andbottom:pitchrate ( ) Thecontrolgainsfor(43)-(4)and(45)-(46)areselectedas = { 10 10 } = { 5 1 300 } = { 0 01 0 001 0 01 } = { 0 1 0 01 0 1 } = { 1 0 5 1 } (4) Thecontrolgainsin(4)wereobtainedusingthesamemethodasinChapter5.In contrasttothissuboptimalapproachused,thecontrolgainscouldhavebeenadjusted usingmoremethodicalapproachesasdescribedinvarioussurveypapersonthetopic [24,46]. The matrixandknowledgeofsomenominal 0matrixmustbeknown.The matrixisgivenby: = 10000000000 01000000000 00100000000 (4) 47

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0 5 10 15 20 25 30 3 5 7200 7400 7600 7800 8000 8200 8400 Velocity (ft/s) 0 5 10 15 20 25 30 3 5 .2 .8 .6 .4 .2 0 0.2 Velocity Error (ft/s)Time (s)Figure4-4:Top:velocity ( ) ,bottom:velocitytrackingerror ( ) fortheoutputvectorof(4),andthe 0matrixisselectedas 0= 32 69 0 017 9 0700023670 11320 316 25 72 0 01119 3900031890251902067 42 84 0 00160 052700042 13092 120 80 0 (4) basedonalinearizedplantmodelaboutsomenominalconditions. TheHSVhasaninitialvelocityofMach 7 5 atanaltitudeof 85 000 .Thevelocity, andvelocitytrackingerrorsareshowninFigure4-4.Theangleofattackandangleof attacktrackingerrorisshowninFigure4-5.Thepitchrateandpitchtrackingerror isshowninFigure4-6.Thecontrole ortrequiredtoachievetheseresultsisshownin Figure4-7.Inadditiontotheoutputstates,o therstatessuchasaltitudeandpitchangle areshowninFigure4-8.ThestructuralmodesareshowninFigure4-9. 4.5Conclusion Thisresultrepresentsthe rsteverapplicationofacontinuous,robustmodelreferencecontrolstrategyforahypersonicvehiclesystemwithadditiveboundeddisturbances 48

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0 5 10 15 20 25 30 3 5 0 1 2 AoA (deg) 0 5 10 15 20 25 30 3 5 .01 0 0.01 0.02 0.03 0.04 0.05 0.06 AoA Error (deg)Time (s)Figure4-5:Top:angleofattack ( ) ,bottom:angleofattacktrackingerror ( ) 0 5 10 15 20 25 30 35 .5 .5 0 0.5 1 1.5 Pitch Rate (deg/s) 0 5 10 15 20 25 30 35 .2 .15 .1 .05 0 0.05 0.1 0.15 Pitch Rate Error (deg/s)Time (s) Figure4-6:Top:pitchrate ( ) ,bottom:pitchratetrackingerror ( ) 49

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0 5 10 15 20 25 30 3 5 0 0.5 1 1.5 Fuel Ratio f 0 5 10 15 20 25 30 3 5 10 15 20 25 Elevator (deg) 0 5 10 15 20 25 30 3 5 0 10 20 Canard (deg)Time (s)Figure4-7:Top:fuelequivalenceratio .Middle:elevatorde ection .Bottom:Canard de ection 0 5 10 15 20 25 30 35 8 8.1 8.2 8.3 8.4 8.5 x 104 Altitude (ft) 0 5 10 15 20 25 30 35 0 1 2 3 Pitch Angle (deg)Time (s) Figure4-8:Top:altitude ( ) ,bottom:pitchangle ( ) 50

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0 5 10 15 20 25 30 3 5 0 20 40 1 0 5 10 15 20 25 30 3 5 0 5 10 2 0 5 10 15 20 25 30 3 5 0 5 3Time (s)Figure4-9:Top: 1structuralelasticmode 1.Middle: 2structuralelasticmode 2. Bottom: 3structuralelasticmode 3. andaerothermoelastice ects,wherethecontrolinputismultipliedbyanuncertain,columnde cient,parameter-varyingmatrix.Apot entialdrawbackoftheresultisthatthe controlstructurerequiresthattheproduct oftheoutputmatrixwiththenominalcontrol matrixbeinvertible.Fortheoutputmatrixandnominalmatrix,theelevatorandcanard de ectionanglesandthefuelequivalenceratiocanbeusedfortrackingoutputssuchas thevelocity,angleofattack,andpitchrateorvelocityandthe ightpathangle,orvelocity, ightpathangleandpitchrate.Yet,thesecontrolscannotbeappliedtosolvethe altitudetrackingproblembecausethealtitudeisnotdirectlycontrollableandtheproduct oftheoutputmatrixwiththenominalcontrolmatrixissingular.However,theintegrator backsteppingapproachthathasbeenexaminedinotherrecentresultsforthehypersonic vehiclecouldpotentiallybeincorporatedinthecontrolapproachtoaddresssuchobjectives.ALyapunov-basedstabilityanalysisisprovidedtoverifytheexponentialtracking result.Althoughthecontrollerwasdevelopedusingalinearparametervaryingmodelof thehypersonicvehicle,simulationresultsforthefullnonlinearmodelwithtemperature variationsandexogenousdisturbancesillustratetheboundednessofthecontrollerwith 51

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favorabletransientandsteadystatetrackingerrors.TheseresultsindicatethattheLPV modelwithexogenousdisturbancesisareasonableapproximationofthedynamicsforthe controldevelopment. 52

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CHAPTER5 CONTROLPERFORMANCEVARIATIONDUETONONLINEAR AEROTHERMOELASTICITYINAHYPERSONICVEHICLE:INSIGHTSFOR STRUCTURALDESIGN 5.1Introduction Typically,controllersaredevelopedtoachievesomeperformancemetricsforagiven HSVmodel.However,improvedperformanceandrobustnesstothermalgradientscould resultifthestructuraldesignandcontroldesignwereoptimizedinunison.Alongthis lineofreasoningin[16,23],theadvantageofcorrectlyplacingthesensorsisdiscussed, representingamovetowardsimplementingacontrolfriendlydesign.Apreviouscontrol orienteddesignanalysisin[6]statesthatsimultaneouslyoptimizingboththestructural dynamicsandcontrolisanintractableproblem,butthatcontrol-orienteddesignmaybe performedbyconsideringtheclosed-loopperformanceofanoptimalcontrolleronaseries ofdi erentopen-loopdesignmodels.Thebestperformingdesignmodelisthensaidto havetheoptimaldynamicsinthesenseofcontrollability. Knowledgeofthebetterperformingthermalgradientscanprovidedesignengineers insighttoproperlyweighttheHSVsthermal protectionsystemforbothsteady-stateand transient ight.Thepreliminaryworkin[6]providesacontrol-orienteddesignarchitecture byinvestigatingcontrolperformancevariationsduetothermalgradientsusingan Hcontroller.Chapter5seekstoextendthecontrolorienteddesignconcepttoexaminecontrol performancevariationsforHSVmodelsthatincludenonlinearaerothermoelasticdisturbances.Giventhesedisturbances,Chapter5focusesonexaminingcontrolperformance variationsforourpreviousmodelreferencerobustcontrollerin[52]andpreviouschapters toachieveanonlinearcontrol-orientedanalysiswithrespecttothermalgradients.By analyzingthecontrolerrorandinputnormsoverawiderangeoftemperaturepro lesan optimaltemperaturepro lerangeissuggested.Basedonpreliminaryworkdonein[50],a numberoflineartemperaturepro lemodelsareexaminedforinsightintothestructural design.Speci cally,thefullsetofnonlinear ightdynamicswillbeusedandcontrole ort, 53

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errors,andtransientssuchassteady-statetimeandpeaktopeakerrorwillbeexamined acrossthedesignspace. 5.2DynamicsandController TheHSVdynamicsusedinthischapterarethesameisinChapter3andequations (3)-(36).SimilarlyasintheresultsinC hapter4,thedynamicsin(3)-(3)are reducedtothelinearparametermodelusedin(2)and(22)with = =2 .Forthe control-orienteddesignanalysis,anumberofdi erentlinearpro lesarechosen[6,50] withvaryingnoseandtailtemperaturesasillustratedinFigure5-1.Thissetofpro les de nethespacefromwhichthecontrol-orientedanalysiswillbeperformed.Asseenin Figure5-1,thetemperaturepro lesarelinearanddecreasingtowardsthetail.These pro lesarerealisticbasedonshockformationatthefrontofthevehicleandthatthe temperaturesarewithintheexpectedrangeforhypersonic ight.Basedonprevious 1 2 3 4 5 6 7 8 9 100 200 300 400 500 600 700 800 900 Fuselage StationTemperature (F)Figure5-1:HSVsurfacetemperaturepro les. [450 900 ] ,and [100 800 ] controldevelopmentin[52]andinthepreviousChapters,thecontrolinputisdesignedas = ( 0) 1[( + 3 3) ( ) ( + 3 3) (0)+ ( )] (51) 54

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where ( ) R2isanimplicitlearninglawwithanupdaterulegivenby ( )= k ( ) k ( ( ))+(+ 3 3) ( )+ ( ( )) (52) where R2 2denotepositivede nite,diagonalconstantcontrolgain matrices, 0 R11 2representsaknownnominalinputmatrix, ( ) denotesthe standardsignumfunctionwherethefunctionisappliedtoeachelementofthevector argument,and 2 2denotesa 2 2 identitymatrix. 5.3OptimizationviaRandomSearchandEvolvingAlgorithms Foreachoftheindividualtemperaturepro lesexamined,thecontrolgains and in(51)-(5)wereoptimizedforthespeci cplantmodelusinga combinationofrandomsearchandevolvingalgorithms.Sinceboththeplantmodel simulationdynamicsandthecontrolschemeitselfarenonlinear,traditionalmethodsfor lineargaintuningoptimizationcouldnotbeused.Theselectedmethodisacombination ofacontrolgainrandomsearchspace,combinedwithanevolvingalgorithmscheme whichallowsthesearchto ndanearestsetofoptimalcontrolgainsforeachindividual plant.Thismethodallowsonenear-optimalcontroller/planttobecomparedtotheother near-optimalcontroller/plantsandprovi desamoreaccuratewayofcomparingcases. The rststepinthecontrolgainoptimizationsearchisarandominitialization.For thisnumericalstudy, 1000 randomlyselectedsetsofcontrolgainsareusedforagiven plantmodel.A 1000 initialrandomsetwaschosentoprovidesu cientsamplingto insureglobalconvergence.Thefollowingsectionhasaspeci cexamplecaseforoneof thetemperaturepro les.Afterthe 1000 controlgainsetsareselected,allthesetsare simulatedonthegivenplantmodelandthecontrollerin(5)and(52)isappliedto trackacertaintrajectoryaswellasrejectdisturbances.Thetrajectoryanddisturbances werechosenthesamethroughouttheentirestudysothattheonlyvariationswillbedue totheplantmodelandcontrolgains.Theexamplecasesectionexplicitlyshowsboththe desiredtrajectoryandthedisturbancesinjected. 55

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Afterthe 1000 initialrandomcontrolgainsearchisperformed,thetop veperformingsetsofcontrolgainsarechosenastheseedsfortheevolvingalgorithmprocess.This processisrepeatedforfourgenerations,eachwiththebest veperformingsetsofcontrol gainsateachstep.Allevolvingalgorithmshavesomeorallofthefollowingcharacteristics:elitism,crossover,andrandommutation.Thisparticularnumericalstudyusesall threeasfollows.Thebest veperformingsetsineachsubsequentgeneration,arechosen aseliteandmoveontothenextiterationstep.Fromthose ve,eachsetofcontrolgains isaveragedwithallotherpermutationsofcontrolgainsintheeliteset.Forinstance,if parent #1 isaveragedwith #2 toformano springsetofcontrolgains.Parent #1 is alsoaveragedwithparent #3 foraseparatesetofo springcontrolgains.Inthisway,all combinationsofcrossoverareperformed.Thepermutationsofthe veeliteparentsyielda totalof 10 o spring. Thenextgenerationcontainsthe veeliteparentsfromthegenerationbefore, aswellasthe 10 crossovero spring,foratotalof 15 .Eachofthese 15 setsofcontrol gainsisthenmutatedbyacertainpercentage.Basedonpreliminarynumericalstudies performedonthisspeci cexample,therandommutationswerechosentobe 20% forthe rsttwogenerationsand 5% forthe naltwogenerations.Thisproducedbothglobal searchinthebeginning,andre nementattheendoftheoptimizationprocedure.The setof 15 remains,withtheadditionof 20 mutatedsetsforeachofthe 15 .Thisgivesa totalcontrolgainsetforthenextgenerationofsearchof 315 .Asstated,therearefour evolvinggenerationsafterthe rst 1000 randomcontrolsets.Thecombinednumberof simulationswithdi erentcontrolgainsperformedforasingletemperaturepro lecaseis 2260 .Theseparticularnumberswerechosenbasedonpreliminarytrialoptimizationcases, withthegoaltoprovidesu cientsearchtoachieveconvergenceofaminimumforthecost function.Thefollowingsectionillustratestheentireprocedureforasingletemperature pro lecase. 56

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Thecostfunctionisdesignedsuchthattheerrorsandcontrolinputsarethesame orderofmagnitudes,sothattheycanmoreeasilybeaddedandinterpreted.Thisis importantbecauseforexample,thedesiredvelocityishigh(inthethousandsofft/s)and thedesiredpitchrateissmall(fractionofradians).Explicitly,thecostfunctionistakenas thesumofthecontrolanderrornormsandisscaledas = 100 1000180 2(53) and = 180 10 2(54) where ( ) ( ) R arethevelocityandpitchrateerrors,respectively,and ( ) ( ) R aretheelevatorandfuelratiocontrolinputs,respectively,and kk2denotesthestandard 2 -norm.Thecombinedcostfunctionisthesumoftheindividual componentsandcanbeexplicitlywrittenas = + (55) where isthecostvalueassociatedwithallsubsequentoptimalgainselection. 5.4ExampleCase TheHSVparametersusedinthesimulationare =75 000 = 86723 2,and =32 174 2 asde nedin(31)-(36).Toillustratehowtherandom searchandevolvingoptimizationalgorithmswork,thissectionisprovidedasadetailed example.Firsttheoutputtrackingsignalanddisturbancesareprovided,followedbythe optimizationandconvergenceprocedure.Thegoalofthissectionistodemonstratethat thespeci cnumberofelites,o spring,mutations,andgenerationslistedintheprevious sectionarejusti edinthatthecostfunctionshowsasymptoticconvergencetoaminimum. ThedesiredtrajectoryisshowninFigure5-2andthedisturbanceisdepictedinFigure 5-3,wherethemagnitudesarechosenbasedonpreviousanalysisperformedin[52].The examplecaseisbasedonatemperaturepro lewith =350 and =200 .For 57

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0 2 4 6 8 10 .5 0 0.5 1 Pitch Rate (deg./s) 0 2 4 6 8 10 7800 7850 7900 7950 Time (s)Velocity ft/s Figure5-2:Desiredtrajectories:pitchrate (top)andvelocity (bottom). 0 1 2 3 4 5 6 7 8 9 10 0 1 x 10 fVdot (ft/s2) 0 1 2 3 4 5 6 7 8 9 10 0 5 f dot (Deg./s) 0 1 2 3 4 5 6 7 8 9 10 .5 0 0.5 fQdot (Deg./s2) 0 1 2 3 4 5 6 7 8 9 10 .05 0 0.05 fetadot (1/s2)Time (s) Figure5-3:Disturbancesforvelocity (top),angleofattack (secondfromtop),pitch rate (secondfrombottom)andthe 1structuralmode(bottom). 58

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0 2 4 6 8 10 .02 0 0.02 0.04 eQ (deg./s) 0 2 4 6 8 10 .5 .5 0 0.5 Time (s)eV (ft/s) Figure5-4:Trackingerrorsforthepitchrate indegrees/sec(top)andthevelocity in ft/sec(bottom). thisparticularcase,Figure5-4andFigure5-5s howthetrackingerrorsandcontrolinputs, respectively,forthecontrolgains = 11 170 039 61 = 14 550 0224 0 = 25 990 00 618 = 20 70 00 369 = 0 9150 00 898 (56) ThecostfunctionshavevaluesasseeninFigure5-6.InFigure5-6thecontrolinput costremainsapproximatelythesame,butasthecontrolgainsevolve,theerrorcostand hencetotalcostdecreaseasymptotically.The 1 veiterationscorrespondtothetop ve performersinthe rst 1000 randomsample,andeachsubsequent vecorrespondtothe top veforthesubsequentevolutiongenerations.Tolimittheoptimizationsearchdesign space,allsimulationsareperformedwithtwoinputsandtwooutputs.Asindicatedinthe costfunctionslistedin(53)-(55),theinputsincludetheelevatorde ection ( ) andthe fuelratio ( ) ,andtheoutputsarethevelocity ( ) andthepitchrate ( ) 59

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0 2 4 6 8 10 9 9.5 10 10.5 11 e (deg.) 0 2 4 6 8 10 0 0.2 0.4 0.6 0.8 1 Time (s)f Figure5-5:Controlinputsfortheelevator indegrees(top)andthefuelratio (bottom). 0 5 10 15 20 25 1.2 1.4 1.6 1.8 x 105 Total Cost 0 5 10 15 20 25 9.594 9.5945 9.595 9.5955 9.596 x 104 Control Cost 0 5 10 15 20 25 3 4 5 6 7 x 104 Error CostIteration # Figure5-6:Costfunctionvaluesforthetotalcost (top),theinputcost (middle) andtheerrorcost (bottom). 60

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5.5Results Theresultsofthissectioncoverallthetemperaturepro lesshowninFigure5-1.The datapresentedincludesthecostfunctionsaswellasothersteady-stateandtransientdata. Includedinthisanalysisarethecontrolcostfunction,theerrorcostfunction,thepeakto-peaktransientresponse,thetimetosteady -state,andthesteady-statepeak-to-peak, forbothcontrolanderrorsignals.Becausethedatacontainsnoise,asmoothedversion ofeachplotisalsoprovided.Thesmoothedplotsuseastandard2-dimensional ltering, whereeachpointisaveragedwithitsneighbors.Forinstanceforsomevariable ,the averageddataisgeneratedas = (4+ +1 + 1 + +1+ 1) 8 (57) Theaveragingformulashownin(5)isusedfor lteringofallsubsequentdata.Also, notethatthelowerrighttriangleformationisduetothedesignspaceonlycontaining temperaturepro leswherethenoseishotterthanthetail.Thisisduetotheassumption thatbecauseofaerodynamicheatingfromtheextremespeedsoftheHSV,thatthis willalwaysbethecase.Thesetemperaturepro lesrelatetotheunderlyingstructural temperature,notnecessarilytheskinsurfacetemperature.Figure5-7andFigure5-8show thecontrolcostfunctionvalue .Notethatthereisaglobalminimum,howeveralso noteforallofthecontrolnormsthetotalvaluesareapproximatelythesame.Thisdata indicatesthatwhileotherperformancemetricsvariedwidelyasafunctionoftemperature pro le,theoverallinputcostremainsapproximatelythesame.InFigure5-9andFigure 5-10,theerrorcostisshown.Notethatthe reisvariability,butthatthereseemsto bearegionofsmallererrorsinthecoolersectionofthedesignspace.Namely,where [200 600] and [100 250] .Combiningthecontrolcostfunctionwith theerrorcostfunctionyieldsthetotalcostfunction(andits lteredcounterpart)depicted inFigure5-11(andFigure5-12,respectively).Theimportanceofthisplotisthatthe totalcostfunctionwasthecriteriaforwhichthecontrolgainswereoptimized.Inthis 61

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 9.5938 9.594 9.5942 9.5944 9.5946 9.5948 9.595 9.5952 9.5954 9.5956 x 104 Figure5-7:Controlcostfunction dataasafunctionoftailandnosetemperature pro les. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 9.5942 9.5944 9.5946 9.5948 9.595 9.5952 9.5954 9.5956 x 104 Figure5-8:Controlcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les. 62

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 3.5 4 4.5 5 x 104 Figure5-9:Errorcostfunction dataasafunctionoftailandnosetemperatureproles. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 4 4.2 4.4 4.6 4.8 5 5.2 5.4 x 104 Figure5-10:Errorcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les. 63

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 1.3 1.35 1.4 1.45 1.5 x 105 Figure5-11:Totalcostfunction dataasafunctionoftailandnosetemperatureproles. sense,thetotalcostplotsrepresentwherethetemperatureparametersarebestsuitedfor controlbasedonthegivencostfunction.Sincethecostofthecontrolinputisrelatively constant,thetotalcostlargelyshowsthesamepatternastheerrorcost.Inadditionto theregionbetween [200 600] and [100 250] ,therealsoseemstobe aregionbetween =900 and [600 900] ,wheretheperformanceisalso improved. Thecontrolcost,errorcost,andtotalcos twereimportantintheoptimizationof thecontrolgainsandwereusedasthecriteriaforselectingwhichgaincombinationwas considerednearoptimal.However,therearepotentiallyotherperformancemetricsof value.Inadditiontotheoptimizationcosts,thepeak-to-peaktransienterrors,timeto steady-state,andsteady-statepeak-to-peakerrorswereexaminedforfurtherinvestigation. Thepeak-to-peaktransienterrorisproducedbytakingthedi erencefromthemaximum andminimumtransienttrackingerrors.T hepeak-to-peakerrorforthepitchrate ( ) isplottedinFigure5-13andFigure5-14,andthepeak-to-peakforthevelocity ( ) is 64

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 1.35 1.4 1.45 1.5 x 105 Figure5-12:Totalcostfunction data( ltered)asafunctionoftailandnosetemperaturepro les. plottedinFigure5-15andFigure5-16.Thepitchratepeak-to-peakerrorsdonothave alargevariationforthedi erentplants,otherthananoticeablepoorperformingregion around =550 and =450 .Thevelocitypeak-to-peakhasaminimum aroundthesimilar [200 600] and [100 250] .Thevelocitypeak-topeakhasminimumswhenthepitchratehasmaximums,indicatingadegreeoftradeo betweenbettervelocityperformance,butworsepitchrateperformance,andviceversa. Anexaminationofthetimetosteady-stateplotsforpitchrateandvelocityshownin Figures5-17-5-20indicatesrelativelysimilartransienttimes,withafewoutliers.Having littlevariationmeansthatalltheplantmodelsaresimilarinthetransienttimeswith thisparticularcontroldesign.Thetimetosteady-stateiscalculatedbylookingatthe transientperformanceandextractingthetimeittakesfortheerrorsignalstodecaybelow thesteady-statepeak-to-peakerrorvalue. 65

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.15 0.2 0.25 0.3 0.35 0.4 Figure5-13:Peak-to-peaktransienterrorforthepitchrate ( ) trackingerrorin deg./sec.. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.15 0.2 0.25 0.3 0.35 0.4 Figure5-14:Peak-to-peaktransienterror( ltered)forthepitchrate ( ) trackingerror indeg./sec.. 66

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 1.45 1.5 1.55 1.6 1.65 1.7 Figure5-15:Peak-to-peaktra nsienterrorforthevelocity ( ) trackingerrorinft/sec.. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 1.5 1.55 1.6 1.65 1.7 Figure5-16:Peak-to-peaktransienterror( ltered)forthevelocity ( ) trackingerrorin ft./sec.. 67

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Figure5-17:Timetosteady-stateforthepitchrate ( ) trackingerrorinseconds. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Figure5-18:Timetosteady-state( ltered)forthepitchrate ( ) trackingerrorinseconds. 68

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.5 1 1.5 2 2.5 3 Figure5-19:Timetosteady-stateforthevelocity ( ) trackingerrorinseconds. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.5 1 1.5 2 2.5 3 Figure5-20:Timetosteady-state( ltered)forthevelocity ( ) trackingerrorinseconds. 69

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0.022 Figure5-21:Steady-statepeak-to-peakerrorforthepitchrate ( ) indeg./sec.. Finally,thesteady-statepeak-to-peakerrorvaluescanbeexaminedforbothoutput signals.Thesteady-statepeak-to-peakerrorsarecalculatedbywaitinguntiltheerror signalfallstowithinsomenon-vanishingsteady-stateboundaftertheinitialtransients havedieddown,andthenmeasuringthemaximumpeak-to-peakerrorwithinthat bound.Theplotsforsteady-statepeak-to-peakerrorforthepitchrateandvelocityare showninFigures5-21-5-24.Thesteady-statepeak-to-peakerrorsshowaminimumin thesimilarregionasseenforotherperformancemetrics,i.e. [200 600] and [100 250] Bynormalizingallofthepreviousdataabouttheminimumofeachsetofdata,and thenaddingtheplotstogether,acombinedplotisobtained.Thisplotassumesthatthe designerweightseachoftheplotse qually,butthemethodcouldbemodi edifcertain aspectsweredeemedmoreimportantthanothers.Explicitly,datafromeachmetricwas combinedasaccordingto = 1 P1( ) min( ( )) (58) 70

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0.022 Figure5-22:Steady-st atepeak-to-peakerror( ltered)forthepitchrate ( ) indeg./sec.. Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 2 4 6 8 10 12 x 10 Figure5-23:Steady-statepeak-to-peakerrorforthevelocity ( ) inft./sec.. 71

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 2 4 6 8 10 12 x 10 Figure5-24:Steady-statepeak-to-peakerror( ltered)forthevelocity ( ) inft./sec. where isthenewcombinedandnormalizedtemperaturepro ledata, isthenumberof datasetsbeingcombined,and arethelocationcoordinatesofthetemperaturedata. Figure5-25showsthiscombinationofcontrolcost,errorcost,peak-to-peakerror,timeto steady-state,andsteady-statepeak-to-peakerrorforbothpitchrateandvelocitytracking errors.Byexaminingthiscostfunction,anoptimalregionbetween [200 600] and [100 250] isdetermined. Inaddition,optimalregionsforthecontr olgainscanbeexamined.Thecontrolgains usedforthisproblemareshownin(5)and(5)havingtheform = 10 0 2 = 10 0 2 = 10 0 2 = 10 0 2 = 10 0 2 (59) Byexaminingthecontrolgainsthemaximum,minimum,mean,andstandard deviationcanbecomputedforallsetsofcontrolgainsfoundtobenearoptimal.Table5-1 72

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Tail Temp (F)Nose Temp (F) 100 200 300 400 500 600 700 800 900 100 200 300 400 500 600 700 800 900 2 3 4 5 6 7 Figure5-25:Combinedoptimization chartofthecontrolanderrorcosts,transientand steady-statevalues. Table5-1:OptimizationControlGainSearchStatistics 1212121212 Mean25.3536.6016.07265.328. 389.6527.4314.120.9720.8958 Std.7.727.647.0585.613.17.9813.510.60.15650.133 Max44.655.353.6423.557.336.462.139.11.3181.201 Min7.143.586.309.7620.3600.0500.3920.1100.6580.6640 showsthecontrolgainstatistics.Thisdataisusefulindescribingtheoptimalrangefor whichcontrolgainswereselected.Byknowingtheregionofnearoptimalattractionfor thecontrolgains,afuturesearchcouldbecon nedtothatregion.Thestandarddeviation alsosayssomethingaboutthesensitivityofthecontrol/aircraftdynamics,wherelarger standarddeviationsmeanthatparticulargainhaslesse ectontheoverallsystemand viceaversa. 5.6Conclusion Acontrol-orientedanalysisofthermalgradientsforahypersonicvehicle(HSV) ispresented.ByincorporatingnonlineardisturbancesintotheHSVmodel,amore representativecontrol-orientedanalysiscanbeperformed.Usingthenonlinearcontroller developedinChapter2andChapter4,performancemetricswerecalculatedforanumber 73

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ofdi erentHSVtemperaturepro lesbasedonthedesignprocessinitiallydeveloped in[6,50].Resultsfromthisanalysisshowthatthereisarangeoftemperaturepro les thatmaximizesthecontrollere ectiveness.Forthisparticularstudy,therangewas [200 600] and [100 250] Inaddition,thisresearchhasshown therangeofcontrolgains,usefulforfuturedesignandnumericalstudies.ThiscontrolorientedanalysisdataisusefulforHSVstructuraldesignsandthermalprotectionsystems. Knowledgeofadesirabletemperaturepro leandcontrolgainswillallowengineers anddesignerstobuildaHSVwiththeproperthermalprotectionthatwillkeepthe vehiclewithinadesiredoperatingrangebasedoncontrolperformance.Inaddition,this numericalstudyprovidesinformationthatcanbefurtherusedinmoreelaborateanalysis processesanddemonstratesonepossiblemethodforobtainingperformancedatafora givencontrolleronthecompletenonlinearHSVmodel. 74

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CHAPTER6 CONCLUSIONSANDFUTUREWORK 6.1Conclusions AnewtypeoncontrollerisdevelopedforLPVsystemsthatrobustlycompensates fortheunknownstatematrix,disturbances,andcompensatesfortheuncertaintyinthe inputdynamicinversion.Incomparisonwithpreviousresults,thisworkpresentsanovel approachincontroldesignthatstandsoutfromtheclassicalgainschedulingtechniques suchasstandardscheduling,theuseofLMIs,andthemorerecentdevelopmentofLFTs, includingtheirnon-convex -typeoptimizationmethods.Classicalproblemssuchasgain schedulingsu erfromstabilityissuesandtherequirementthatparametersonlychange slowly,limitingtheirusetoquasi-linearcases.LMIsuseconvexoptimization,buttypically requiretheuseofnumericaloptimizationschemesandareanalyticallyintractableexcept inrarecases.LFTsfurtherthecontroldesignforLPVsystemsbyusingsmallgaintheory,howevertheycannotdealexplicitlywithuncertainparameters.Tohandleuncertain parameters,theLFTproblemisconvertedi ntoanumericaloptimizationproblemsuch as -typeoptimization. -typeoptimizationisnon-convexandthereforesolutionsmay notbefoundevenwhentheyexist.Therobustdynamicinversioncontroldevelopedfor uncertainLPVsystemsalleviatestheseproblems.Aslongassomeknowledgeoftheinput matrixisknownandcertaininvertabilityrequirementsaremetthenastabilizingcontrolleralwaysexists.Proofsprovidedshowthatthecontrollerisrobusttodisturbances, statedynamics,anduncertainparametersbyusinganewrobustcontrollertechniquewith exponentialstability. CommonapplicationsforLPVsystemsare ightcontrollers.Thisisbecausehistorically ighttrajectoriesvaryslowlywithtimeandarewellsuitedtothepreviously mentionedLPVcontrolschemessuchasgainscheduling.Recentadvancesintechnology andaircraftdesignaswellasmoredynamicanddemanding ightpro leshaveincreased thedemandonthecontrollers.Inthesedemandingdynamicenvironments,parameters 75

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nolongerchangeslowlyandmaybeunknownoruncertain.Thisrenderspreviouscontroldesignslimiting.Motivatedbythisfactandspeci callyusingthedynamicsofan air-breathingHSV,thedynamicsareshowntobemodeledasanLPVsystemwithuncertaintiesanddisturbances.Thisworkmotivatesthedesignandtestingoftherobust dynamicinversioncontrolleronatemperaturevaryingHSV.Usingunknowntemperature pro les,whilesimultaneouslytrackinganoutputtrajectory,therobustcontrollerisshown tocompensateforunknowntime-varyingpara metersinthepresenceofdisturbancesfor theHSV.Usingonesetofcontrolgainsitwasshownthatstablecontrolwasmaintained overtheentiredesignspacewhileperformingmaneuvers.EventhoughthecontrolwasdevelopedforLPVsystems,thesimulationresultsareperformedonthefullnonlinearHSV ightandstructuraldynamics,hencevalidatedthecontrol-orientedmodelingassumptions. Finally,anumericaloptimizationschemewasperformedonthesameHSVmodel, usingacombinationofrandomsearchandevolvingalgorithmstoproducedynamic optimizationdataforthecombinedvehicleandcontroller.Regionsofoptimalitywere showntoprovidefeedbacktodesignengineersonthebestsuitabletemperaturepro le parameterspace.Toremoveambiguity,thecontrollerforeachindividualtemperature pro lecasewasoptimallytunedandthetrackin gtrajectoryanddisturbanceswerekept thesame.Analyticalmethodsdonotexistforoptimalgaintuningnonlinearcontrollerson nonlinearsystemsHence,anumericaloptimizingschemewasdeveloped.Bystrategically searchingthecontrolgainspacevalueswereobtained,andtheperformancemetricsat thatpointwerecomparedacrossthevehicledesignspace.Thisworkmaybeusefulfor futuredesignproblemsforHSVswherethestructuralanddynamicdesignareperformed inconjunctionwiththecontroldesign. 6.2Contributions AnewrobustdynamicinversioncontrollerwasdevelopedforgeneralperturbedLPV systems.Thecontroldesignrequiresknowledgeofabestguessinputmatrixandat leastasmanyinputsastrackedoutputs.Inthepresenceanunknownstatematrix, 76

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parameters,anddisturbance,andwithanuncertaininputmatrix,thedeveloped controldesignprovidesexponentialtrackingprovidedcertainassumptionsaremet. Thedevelopedcontrolmethodtakesadi erentapproachtotraditionalLPVdesign andprovidesaframeworkforfuturecontroldesign. BecausetheassumptionsrequiredofthecontrolleraremetbytheHSV,anumerical simulationwasperformed.AfterreducingtheHSVnonlineardynamicstothat ofanLPVsystemmotivationwasprovidedtoimplementthecontrollerdesigned. AsimulationisprovidedwherethefullnonlinearHSVdynamicsareused.The simulationdemonstratesthee cacyoftheproposedcontroldesignonthisparticular HSVapplication.Awiderangeoftemperaturevariationswereusedandtracking controlwasimplementedtodemonstratetheperformanceofthecontroller. FurtherperformanceevaluationwasconductedbydesigninganoptimizationproceduretoanalyzetheinterplaybetweentheHSVdynamics,temperatureparameters, andcontrollerperformance.Anumberofdi erenttemperatureplantmodelsfor HSVwerenearoptimallytunedusingacombinationofarandomsearchandevolvingalgorithms.Next,thecontrolperformancewasevaluatedandcomparedtothe otherHSVtemperaturemodels.Comparativeanalysisisprovidedthatsuggests regionswherethetemperaturepro lesoftheHSVinconjunctionwiththeproposed controldesignachieveimprovedperformanceresults.Theseresultsmayprovide insighttostructuralsystemsdesignersforHSVsaswellasprovidesca oldingfor futurenumericaldesignoptimizationandcontroltuning. 6.3FutureWork Therobustdynamicinversioncontroldesigninthisdissertationrequiresknowledge ofthesignoftheerrorsignalderivativeterms.Whilethesemeasurementsmaybe availableforspeci capplications,thisunderlyingnecessityreducesthegeneralityof thecontroller.Futureworkcouldfocuson removingthisrestriction,andproducing anoutputfeedbackonlyrobustdynamicinversioncontrol. 77

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Anotherrequirementofthecontroldesignistherequirementofthediagonal dominanceofthebestguessfeedforwardinputmatrix.Whilethisrequirementis notunreasonablebecauseitonlyrequiresthattheguessbewithinthevicinityof theactualvalue,futureworkcouldfocusonrelaxingthatrequirement.Alleviating thisrestrictioncouldpotentiallybedonebyusingpartialadaptationlawswhile simultaneouslyusingrobustalgorithmstocountertheparametervariations. Itwasshownthatthecontrollerdevelopedisabletotrackinner-loopstatesforthe HSV,howeveritwouldbebene cialtoadaptthisinnerloopcontroldesigntoan outerloop ightplanningcontroller.Inthisway,morepracticalplannedtrajectories canbetracked(e.g.,altitude)byusingtheinnerloopofpitchrateandpitchangle control.Additionally,thissameresultcanbeattainedbyusingbackstepping techniques.Bybacksteppingthroughotherstatedynamics(e.g.,altitude)andinto thecontroldynamics(e.g.,pitchrate),acombinedcontrollercouldbedeveloped. Thetemperatureandcontrolgainoptimizationprovidesagoodframeworkfor ndingHSVdesignswithincreasedperformance.Itwouldbeinterestinginfuture worktore-analyzetheoptimalcontrolgainspace,andseeifitcouldbeconvergedto asmallerset.Iftheoptimalsetcouldbefurtherconverged,thenthroughnumerous iterationsaverypreciseandnarrowrangemaybefound.Findingamoreoptimal designspacemayaidinfuturestructuraloptimizationsearches. Itwouldalsobebene cialfortheoptimizationworktohavemoreaccuratenonlinear models.ObtainingbettermodelswillrequireworkingincollusionwithHSV designers.Gettinghighqualityfeedbackonthedesignconstraintsand ight trajectoryconstraintswouldfurtheraidthesearchforoptimalityinregardsto controlgainsandtemperaturepro les.Inaddition,thedynamicscouldbemodeled andsimulatedwithhighercertaintyifmoredetailswereknown.Combiningextra dataonthedynamicsintothecontroldesignwouldhelpfurtherthedevelopmentof actual ightworthyvehicles. 78

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APPENDIXA OPTIMIZATIONDATA Thedatapresentedinthefollowingtablesistherawdatafromtheimagespresented inChapter5.Therowscontainsallofthe in andthecolumnscontainthe in .Emptyspacesareplaceswherethetailtemperatureishigherthanthenose temperature,andareoutsidethedesignspaceofthisworkandommitted. 79

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TableA-1:Totalcostfunction,usedtogenerateFigure5-11and5-12(Part1) 100150200250300350400450500 100144526 150143210145071 200141588140254143397 250141588140254143397142557 300143086140577143199143895142656 350133478145807129636134531141681143496 400143490143396139825140233146708142439140353 450129673141283141577136368143591144789144610149182 500140466139064141863144110145435140439145178142468141932 550143730144033137552140079147113143303139847139083143308 600143730145599138430140945147159143625140785139202144040 650143884137784145621144958151291148236144025145853144782 700146708142439140353138181143955145086144610149182129812 750144610149182129812140633144027146527140466139965146527 800140845146015139499140904143730129426146864144790135440 850141959138801142931145923138328145212142817140848140940 900143955145086144610149182129812140633144027146527140466 TableA-2:Totalcostfunction,usedtogenerateFigure5-11and5-12(Part2) 550600650700750800850900 550144322 600144420144857 650145109141262127435 700140633144027146527140466 750140466143396139825140233146708 800144948143418145297135394142384140069 850144253141883148014136336143641145803145941 900143828147566129349138888131875142296135461134603 80

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TableA-3:Controlinputcostfunction,usedtogenerateFigure5-7and5-8(Part1) 100150200250300350400450500 10095951 1509594995953 200959489594995952 25095948959499595295952 3009594995950959539595395949 350959529595295957959579595395953 40095948959539595795953959549595295953 4509595195946959529595095946959539595295953 500959509595095954959489595395949959529594895953 550959529595095957959499595495952959489593795949 600959529594995950959529595195952959489593795952 650959539595295953959539595395954959499595395950 700959549595295953959469595395953959529595395952 750959529595395952959539595395954959509595095954 800959539595395953959499595395952959539595295957 850959529595395953959539595395957959539594995953 900959539595395952959539595295953959539595495950 TableA-4:Controlinputcostfunction,usedtogenerateFigure5-7and5-8(Part2) 550600650700750800850900 55095952 6009595295952 650959499595295952 70095953959539595495950 7509595095953959579595395954 800959539594995953959499595395949 85095953959529595395952959539595295953 9009594895953959519594095949959539594695950 81

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TableA-5:Errorcostfunction,usedtogenerateFigure5-9and5-10(Part1) 100150200250300350400450500 10048574 1504726049118 200456394430447444 25045639443044744446605 3004713644626472454794246706 350375254985533679385744572747542 40047541474434386744280507544648744400 4503372145337456254041847644488354865853228 500445164311445908481624948244490492254651945979 550477774808241594441295115947350438984314647358 600478574964942479449925120847673448374326448088 650479304183149667490055533752281480754990048831 700507544648744400422354800249133486585322833860 750486585322833860446804807450572445164401550572 800448925006243546449544777633474509114883739482 850460074284846978499694237549254468644489844986 900480024913348658532283386044680480745057244516 TableA-6:Errorcostfunction,usedtogenerateFigure5-9and5-10(Part2) 550600650700750800850900 55048370 6004846748905 650491604531031482 70044680480745057244516 7504451647443438674428050754 800489954746949343394384643044120 85048299459315206040384476884985049987 9004788051613333974294735925463423951438653 82

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TableA-7:Pitchrate,peak-to-peakerror,usedtogenerateFigure5-13and5-14(Part1) 100150200250300350400450500 1000.1951 1500.16780.1377 2000.20570.17220.1421 2500.20570.17220.14210.1842 3000.14500.25880.13650.18030.1669 3500.13740.17120.14270.13720.15360.1601 4000.13990.15300.15000.18350.13360.14480.1849 4500.15350.24780.12780.22140.28390.14210.14340.1436 5000.21750.21970.15050.17280.15900.16720.14810.21740.1292 5500.23380.16240.14910.14300.13430.18670.18480.44580.2287 6000.17380.20850.14650.15480.20710.13940.17990.45610.1471 6500.15600.13270.18570.15530.14060.14000.13740.16650.1530 7000.13360.14480.18490.29280.15390.14150.14340.14360.1530 7500.14340.14360.15300.15730.16920.16550.21750.22000.1655 8000.15100.13310.15020.15730.15950.19160.16550.18320.1473 8500.19390.14680.15320.15320.19920.14640.14320.20640.1409 9000.15390.14150.14340.14360.15300.15730.16880.16550.2175 TableA-8:Pitchrate,peak-to-peakerror,usedtogenerateFigure5-13and5-14(Part2) 550600650700750800850900 5500.1787 6000.19600.1309 6500.17190.19470.1353 7000.15730.16920.16550.2175 7500.29120.15300.16120.18350.1939 8000.14710.26730.13560.13540.16580.1833 8500.16410.13230.13980.15070.14380.17330.1395 9000.14910.14930.14990.39290.22760.18220.29410.2615 83

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TableA-9:Pitchrate,steady-statepeak-to-peakerror,usedtogenerateFigure5-21and 5-22(Part1) 100150200250300350400450500 1000.0170 1500.01630.0170 2000.01790.01560.0178 2500.01790.01560.01780.0163 3000.01760.01660.01730.01840.0167 3500.00270.02330.00160.00280.01500.0173 4000.02320.01690.00480.01490.01860.01540.0144 4500.00120.02000.01730.00310.01920.01830.02210.0221 5000.00530.00480.00700.01770.01660.01660.01670.01840.0160 5500.01960.01690.00360.01660.01730.01930.01590.00320.0185 6000.01730.01860.00450.01630.01780.01830.01540.00340.0185 6500.02220.00270.01640.01520.01780.02100.01730.02020.0171 7000.01860.01540.01440.00390.01720.01910.02070.02110.0008 7500.02070.02110.00080.01540.02020.01710.00530.00560.0171 8000.01650.02130.01460.01510.01630.00290.02140.01660.0050 8500.01790.00300.01610.01700.00490.01740.01500.01760.0074 9000.01820.02020.02210.02210.00080.01600.02040.01690.0053 TableA-10:Pitchrate,steady-statepeak-topeakerror,usedtogenerateFigure5-21and 5-22(Part2) 550600650700750800850900 5500.0188 6000.01800.0210 6500.01930.01850.0019 7000.01540.02020.01710.0053 7500.00480.01690.00460.01490.0180 8000.01710.01870.01630.00260.01700.0193 8500.01710.01980.01990.00340.01630.02040.0173 9000.02260.01790.00090.00580.00150.01830.00410.0021 84

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TableA-11:Pitchrate,timetosteady-state,usedtogenerateFigure5-17and5-18(Part 1) 100150200250300350400450500 1000.439 1500.4290.433 2000.4120.3380.451 2500.4710.2870.4720.541 3000.4500.3810.5180.5180.515 3500.3940.4990.4310.5400.5120.472 4000.4710.5420.4120.4940.4020.5110.474 4500.5560.4070.4050.4750.4730.4440.4820.519 5000.5800.6130.5420.4500.4240.4730.4040.4960.473 5500.4360.3580.4440.4610.4420.4680.4500.6180.427 6000.4470.4930.5180.4750.4570.5130.5060.5930.473 6500.5180.5700.4890.4570.6010.4750.4950.5330.408 7000.4250.4710.4490.6770.4970.4640.4490.4960.450 7500.4970.4962.1430.4740.4530.4450.5640.6920.470 8000.4420.4910.4500.4710.4940.4260.4730.4950.587 8500.4320.5760.4700.5270.5930.4640.4770.5170.572 9000.4940.4920.4970.4270.4860.4960.4670.4550.537 TableA-12:Pitchrate,timetosteady-state,usedtogenerateFigure5-17and5-18(Part 2) 550600650700750800850900 5500.450 6000.4210.476 6500.4530.4030.423 7000.4720.4710.4450.564 7500.5480.4730.4300.5180.479 8000.4510.4950.4600.4490.4740.522 8500.5030.5370.5580.4040.4780.4950.469 9000.4210.4910.5590.5920.8180.4490.5210.692 85

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TableA-13:Velocity,peak-to-peakerror,usedtogenerateFigure5-15and5-16(Part1) 100150200250300350400450500 1001.5670 1501.66491.6847 2001.64461.66691.5972 2501.64451.66681.59731.5986 3001.68391.66631.63441.60551.6081 3501.55961.59041.54011.53661.55801.6235 4001.58361.60221.49171.59101.67351.53571.5910 4501.56341.72541.59461.44201.70981.60641.58721.5456 5001.46861.46511.53211.59661.65791.63291.54081.60751.5710 5501.58931.74471.48591.64171.65161.61661.56271.42191.5953 6001.65371.59341.53591.59931.70761.60381.59621.42381.5834 6501.59611.61761.63661.64261.40891.61701.72211.59901.6525 7001.67351.53571.59101.43441.59491.58901.58761.54561.5294 7501.58761.54561.52941.59801.60781.69651.46861.46061.6965 8001.59481.62701.58281.68001.62481.51241.60331.60581.5128 8501.58551.52051.59841.66751.62051.54331.59661.68341.6128 9001.59401.58901.58721.54561.52941.59801.60781.69651.4686 TableA-14:Velocity,peak-to-peakerror,usedtogenerateFigure5-15and5-16(Part2) 550600650700750800850900 5501.5814 6001.58431.5756 6501.69161.58451.5754 7001.59801.60781.69651.4686 7501.46621.60221.52541.59101.6436 8001.59511.67371.55721.48521.58731.6127 8501.58301.58171.47351.53051.60271.56701.6405 9001.60251.56931.55421.45851.45811.60601.47211.4935 86

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TableA-15:Velocity,steady-statepeak-topeak,usedtogenerateFigure5-23and5-24 (Part1) 100150200250300350400450500 1000.0037 1500.00880.0050 2000.00180.00360.0046 2500.00190.00380.00470.0039 3000.00370.00300.00340.00350.0131 3500.00160.00330.00100.00260.00150.0066 4000.00350.00280.00130.00290.00590.00210.0031 4500.00020.00170.00270.00040.01050.00320.00140.0031 5000.00210.00170.00150.00270.00370.00690.00220.00140.0027 5500.00690.00320.00120.00370.01030.00220.00460.00260.0035 6000.00380.00410.00220.00330.01080.00180.00380.00270.0035 6500.00350.00100.00700.00450.00410.00510.00400.00840.0066 7000.00590.00210.00310.00240.00280.00390.00140.00400.0008 7500.00140.00400.00080.00290.00370.00550.00210.00220.0055 8000.00350.00940.00380.00340.01180.00090.01260.00330.0008 8500.00280.00030.00750.00680.00060.00450.00240.00230.0016 9000.00230.00310.00310.00080.00310.00400.00990.00210.0027 TableA-16:Velocity,steady-statepeak-topeak,usedtogenerateFigure5-23and5-24 (Part2) 550600650700750800850900 5500.0022 6000.00400.0027 6500.00340.00360.0008 7000.00290.00370.00550.0021 7500.00160.00280.00130.00290.0041 8000.00350.00540.00320.00150.00300.0028 8500.00270.00070.00130.00180.01010.00410.0057 9000.00270.00330.00020.00060.00050.01070.00050.0009 87

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TableA-17:Velocity,timetosteady-state,usedtogenerateFigure5-19and5-20(Part1) 100150200250300350400450500 1002.012 1501.1190.915 2000.5390.2680.496 2500.4980.2840.5280.506 3000.5430.3140.5860.4921.201 3500.3830.5200.4740.4720.5220.701 4000.5010.5150.4030.4920.3551.940.516 4500.7470.5210.4910.8210.5430.4910.5130.637 5000.4940.4720.4840.9830.3780.5020.4810.5420.656 5500.4920.3390.3850.5460.5681.2080.4960.8411.201 6000.5620.5000.4930.5630.5781.0430.4920.7080.712 6500.5621.6810.6270.4000.7050.5211.3961.7600.932 7000.3830.4980.7040.8080.8360.4910.5040.5163.330 7500.4590.5873.3001.3470.5390.6790.4730.4960.680 8000.6780.8170.4590.5381.1140.3090.9290.6750.403 8500.7021.3560.5220.7760.5140.4300.5410.5110.363 9000.7980.4800.5190.6322.8440.8220.5430.6070.518 TableA-18:Velocity,timetosteady-state,usedtogenerateFigure5-19and5-20(Part2) 550600650700750800850900 5500.473 6000.3980.568 6500.5150.5160.519 7000.8210.5200.6790.473 7500.5180.5410.4730.5410.671 8000.4670.7050.7020.3370.8020.550 8500.5420.5640.5180.3000.6880.8180.607 9000.4740.5452.2930.5641.0951.3930.6420.559 88

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REFERENCES [1]L.K.Abbasa,C.Qian,P.Marzocca,G.Zafer,andA.Mostafa,Activeaerothermoelasticcontrolofhypersonicdouble-wedgeliftingsurface, Chin.J.Aeronaut. vol.21,pp.8,2008. [2]P.ApkarianandP.Gahinet,Aconvexch aracterisationofgain-scheduledh-inf controllers, IEEETransactionsonAutomaticControl ,vol.40,pp.853,1995. [3]P.ApkarianandH.D.Tuan,Parameterizedlmisincontroltheory, SIAMJ.Contr. Optim. ,vol.38,no.4,pp. 1241,2000. [4]K.J.AustinandP.A.Jacobs,Applicationofgeneticalgorithmstohypersonic ight control,in IFSAWorldCongr.,NAFIPSInt.Conf. ,Vancouver,BritishColumbia, Canada,July2001,pp.2428. [5]G.J.Balas,Linearparametervaryingcontrolanditsapplicationtoaturbofan engine, Int.J.Non-linearandRobustControl,SpecialissueonGainScheduled Control ,vol.12,no.9,pp.763,2002. [6]S.BhatandR.Lind,Control-orientedanalysisofthermalgradientsforahypersonic vehicle,in Proc.IEEEAm.ControlConf. ,2009. [7]J.M.BiannieandP.Apkarian,Missileautopilotdesignviaamodi edlpvsynthesis technique, Aerosp.Sci.Technol. ,pp.763,1999. [8]M.BolenderandD.Doman,Anon-linearmodelforthelongitudinaldynamicsof ahypersonicair-breathingvehicle,in Proc.AIAAGuid.Navig.ControlConf. ,San Francisco,CA,Aug.2005. [9],Modelingunsteadyheatinge ectsonthestrucutraldynamicsofahypersonic vehicle,in AIAAAtmos.FlightMech.Conf. ,Keystone,CO,Aug.2006. [10]M.A.BolenderandD.B.Doman,Nonlin earlongitudinaldynamicalmodelofan air-breathinghypersonicvehicle, J.SpacecraftRockets ,vol.44,no.2,pp.374, Apr.2007. [11]S.Boyd,L.E.Ghaoui,E.Feron,andV.Balakrishnan,Linearmatrixinequalitiesin systemandcontroltheory .SIAM,1994. [12]A.J.Culler,T.Williams,andM.A.Bolender,Aerothermalmodelinganddynamic analysisofahypersonicvehicle,in AIAAAtmos.FlightMech.Conf. ,HiltonHead, SC,Aug.2007. [13]J.Daafouz,J.Bernussou,andJ.C.Geromel,Oninexactlpvcontroldesignof continuous-timepolytopicsystems, IEEETransactionsonAutomaticControl ,vol. Vol.53,No.7,no.7,August2008. 89

PAGE 90

[14]M.Dinh,G.Scorletti,V.Fromion,andE.Magarotto,Parameterdependenth-inf controlby nitedimensionallmioptimization:applicationtotrade-o dependent control, Int.J.RobustNonlinearContr. ,vol.15,no.9,pp.383,2005. [15]A.Filippov, Di erentialequationswithdiscontinuousright-handside .Netherlands: KluwerAcademicPublishers,1988. [16]L.Fiorentini,A.Serrani,M.A.Bolender,andD.B.Doman,Nonlinearrobust/adaptivecontrollerdesignforanair-breathinghypersonicvehiclemodel,in Proc.AIAAGuid.Navig.ControlConf. ,HiltonHead,SC,Aug.2007. [17],Nonlinearrobustadaptivecontrolof exibleair-breathinghypersonicvehicle, J.Guid.Contr.Dynam. ,vol.32,No.2,pp.402,April2009. [18]P.Gaspa,I.Szaszi,andJ.Bokor,Activesuspensiondesignusinglinearparameter varyingcontrol, Int.J.VehicleAuton.Syst. ,vol.1,no.2,pp.206,2003. [19]T.Gibson,L.Crespo,andA.Annaswamy,Adaptivecontrolofhypersonicvehicles inthepresenceofmodelinguncertainties,in Proc.IEEEAm.ControlConf. ,June 2009. [20]S.Gutman,UncertaindynamicalsystemsaLyapunovmin-maxapproach, IEEE Trans.Autom.Control ,vol.24,no.3,pp.437,1979. [21]J.Heeg,T.A.Zeiler,A.S.Pototzky,andC.V.Spain,Aerothermoelasticanalysisof aNASPdemonstratormodel,in Proc.oftheAIAAStructuresStruct.Dyn.Mater. Conf. ,LaJolla,CA,Apr.1993. [22]A.Helmersson,musynthesisandlftschedulingwithmixeduncertainties,in ProceedingsoftheEuropeanControlConference ,1995,pp.153. [23]P.Jankovsky,D.O.Sigthorsson,A.Se rrani,andS.Yurkovich,Outputfeedback controlandsensorplacementforahypersonicvehiclemodel,in Proc.AIAAGuid. Navig.ControlConf. ,HiltonHead,SC,Aug.2007. [24]N.J.KillingsworthandM.Krstic,PIDtuningusingextremumseeking:online, model-freeperformanceoptimization, IEEEContr.Syst.Mag. ,vol.26,no.1,pp. 70,2006. [25]L.LiandV.A.Ugrinovshii,Robuststabilizationoflpvsystemswithstructured uncertaintyusingminimaxcontrollers,in IEEEConferenceonDecisionandControl 2007. [26]R.Lind,Linearparameter-varyingmodelingandcontrolofstructuraldynamics withaerothermoelastice ects, J.Guid.Contr.Dynam. ,vol.25,no.4,pp.733, July-Aug.2002. 90

PAGE 91

[27]W.MacKunis,K.Kaiser,Z.D.Wilcox,andW.E.Dixon,Globaladaptiveoutput feedbacktrackingcontrolofanunmannedaerialvehicle, IEEETrans.ControlSyst. Technol. ,toappear,2010. [28]W.MacKunis,M.K.Kaiser,P.M.Patre,andW.E.Dixon,Adaptivedynamic inversionforasymptotictrackingofanaircraftreferencemodel,in Proc.AIAA Guid.Navig.ControlConf. ,Honolulu,HI,2008. [29]W.MacKunis,Z.D.Wilcox,K.Kaiser,andW.E.Dixon,Globaladaptiveoutput feedbackMRAC,in Proc.IEEEConf.Decis.ControlChin.ControlConf. ,2009. [30]C.I.MarrisonandR.F.Stengel,Designofrobustcontrolsystemsforahypersonic aircraft, J.Guid.Contr.Dynam. ,vol.21,no.1,pp.58,1998. [31]A.Packard,Gainschedulingvialinearfractionaltransformations, Systems& ControlLetters ,vol.22,no.2,pp.79,1994. [32]J.T.Parker,A.Serrani,S.Yurkovich,M.A.Bolender,andD.B.Doman,Controlorientedmodelingofanair-breathinghypersonicvehicle, J.Guid.Contr.Dynam. vol.30,no.3,pp.856,2007. [33]P.Patre,W.Mackunis,M.Johnson,andW.Dixon,Compositeadaptivecontrolfor Euler-Lagrangesystemswithadditivedisturbances, Automatica ,vol.46,no.1,pp. 140,2010. [34]P.M.Patre,K.Dupree,W.MacKunis,andW.E.Dixon,Anewclassofmodular adaptivecontrollers,partII:Neuralnetworkextensionfornon-LPsystems,in Proc. IEEEAm.ControlConf. ,2008,pp.1214. [35]P.M.Patre,W.MacKunis,K.Dupree,andW.E.Dixon,Anewclassofmodular adaptivecontrollers,partI:Systemswithlinear-in-the-parametersuncertainty,in Proc.IEEEAm.ControlConf. ,2008,pp.1208. [36], RISE-BasedRobustandAdaptiveControlofNonlinearSystems.Boston: Birkhuser,2009,undercontract. [37]P.M.Patre,W.MacKunis,K.Kaiser,andW.E.Dixon,Asymptotictracking foruncertaindynamicsystemsviaamultilayerneuralnetworkfeedforwardand RISEfeedbackcontrolstructure, IEEETrans.Autom.Control ,vol.53,no.9,pp. 2180,2008. [38]P.M.Patre,W.Mackunis,C.Makkar,andW.E.Dixon,Asymptotictrackingfor systemswithstructuredandunstructureduncertainties,in IEEETransactionson ControlSystemsTechnology ,vol.16,No.2,2008,pp.373. [39]P.Pellanda,P.Apkarian,andH.D.Tuan,Missileautopilotdesignviaamultichannellft/lpvcontrolmethod, Int.J.RobustNonlinearControl ,vol.12,pp.1, 2002. 91

PAGE 92

[40]Z.QuandJ.X.Xu,Model-basedlearningcontrolsandtheircomparisonsusing Lyapunovdirectmethod, AsianJ.Control ,vol.4(1),pp.99,2002. [41]A.Serrani,A.Zinnecker,L.Fiorentini,M.Bolender,andD.Doman,Integrated adaptiveguidanceandcontrolofconstrainednonlinearair-breathinghypersonic vehiclemodels,in Proc.IEEEAm.ControlConf. ,June2009. [42]J.ShammaandM.Athans,Gainscheduling:Potentialhazardsandpossible remedies, IEEEControlSystemMagazine ,vol.vol.12,no.no.3,pp.pp.101, 1992. [43]J.S.Shamma,Gain-schedulingmissileautopilotdesignusinglinearparameter varyingtransformations, JournalofGuidance,ControlandDynamics ,vol.16,no.2, pp.256,1993. [44]D.Sigthorsson,P.Jankovsky,A.Serrani,S.Yurkovich,M.Bolender,andD.Doman., Robustlinearoutputfeedbackcontrolofanair-breathinghypersonicvehicle, J. Guid.Contr.Dynam. ,vol.31,No.4,pp.1052,July2008. [45]M.Spillman,P.Blue,S.Banda,andL.Lee,Arobustgain-schedulingexampleusing linearparameter-varyingfeedback,in ProceedingsoftheIFAC13thTriennialWorld Congress ,1996,pp.221. [46]K.strm,T.Hgglund,C.Hang,andW.Ho,Automatictuningandadaptation forPIDcontrollers-asurvey, ControlEng.Pract. ,vol.1,no.4,pp.669,1993. [47]L.F.Vosteen,E ectoftemperatureondynamicmodulusofelasticityofsome structuralallows,in Tech.Rep.4348,LangleyAeronouticalLaboratory,Hampton, VA ,August1958. [48]S.A.W.A.,Strongstabilizationofmimosystems:Anlmiapproach,in Systems, SignalsandDevices ,2009. [49]Q.WangandR.F.Stengel,Robustnonlinearcontrolofahypersonicaircraft, J. Guid.Contr.Dynam. ,vol.23,no.4,pp.577,2000. [50]Z.D.Wilcox,S.Bhat,R.Lind,andW.E.Dixon,Controlperformancevariation duetoaerothermoelasticityinahypersonicvehicle:Insightsforstructuraldesign,in Proc.AIAAGuid.Navig.ControlConf. ,August2009. [51]Z.D.Wilcox,W.MacKunis,S.Bhat,R.Lind,andW.E.Dixon,Robustnonlinear controlofahypersonicaircraftinth epresenceofaerothermoelastice ects,in Proc. IEEEAm.ControlConf. ,St.Louis,MO,June2009,pp.2533. [52],Lyapunov-basedexponentialtrackingcontrolofahypersonicaircraftwith aerothermoelastice ects, J.Guid.Contr.Dynam. ,vol.33,no.4,Jul./Aug2010. [53]T.Williams,M.A.Bolender,D.B.Doman,andO.Morataya,Anaerothermal exiblemodeanalysisofahypersonicvehicle,in AIAAPaper2006-6647 ,Aug.2006. 92

PAGE 93

[54]B.Xian,D.M.Dawson,M.S.deQueiroz,andJ.Chen,Acontinuousasymptotic trackingcontrolstrategyforuncertainnonlinearsystems, IEEETrans.Autom. Control ,vol.49,no.7,pp.1206,Jul.2004. [55]M.Yoshihiko,Adaptivegain-scheduledH-in nitycontroloflinearparameter-varying systemswithnonlinearcomponents,in Proc.IEEEAm.ControlConf. ,Denver,CO, June2003,pp.208. 93

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BIOGRAPHICALSKETCH ZachWilcoxgrewupinYarrowPoint,acityjustoutsideofSeattle,Washington,and livedthereuntilmovingtoFloridatoattendcollegein2001.HereceiveddualBachelor ofSciencedegreesfromtheUniversityofFloridasAerospaceandMechanicalEngineering departmentinthespringof2006.Duringhisundergraduatework,Zachparticipatedasa diveronUFsMensSwimmingDivingTeam.Inaddition,hedidresearchworkforUFs MicroAirVehicle(MAV)groupandparticipatedinInternationalMAVcompetitions.He recievedhisMastersofScienceinAerospaceEngineeringfromUniversityofFloridainthe springof2008.HisDoctoralstudieswereintheNonlinearControlsandRoboticsGroup intheDepartmentofMechanicalandAerospaceEngineeringundertheadvisementofDr. Dixon.HereceivedhisPh.D.inAerospaceEngineeringinAugust2010. 94