Suitable either as a reference or as a text for a graduate course in adaptive control systems, this book is a self-contained compendium of easily implementable adaptive control algorithms that have been developed and applied by the authors for over 10 years. These algorithms do not require explicit process parameter identification and have been successfully applied to a wide variety of engineering problems including flexible structure control, blood pressure control and robotics. In general, these algorithms are suitable for a wide class of multiple input-output control systems containing significant uncertainty as well as disturbances. …mehr
Suitable either as a reference or as a text for a graduate course in adaptive control systems, this book is a self-contained compendium of easily implementable adaptive control algorithms that have been developed and applied by the authors for over 10 years. These algorithms do not require explicit process parameter identification and have been successfully applied to a wide variety of engineering problems including flexible structure control, blood pressure control and robotics. In general, these algorithms are suitable for a wide class of multiple input-output control systems containing significant uncertainty as well as disturbances.
The text has been written so that anyone with a basic linear multivariable systems background will be able to develop and apply the adaptive algorithms to their particular problems.
Inhaltsangabe
1 Introduction.- 1.1 Definition of the Problem.- 1.2 Prologue to Simple Adaptive Control.- 1.3 Background on Adaptive Control Algorithms.- 1.4 Objectives and Overview.- 2 Basic Theory of Simple Adaptive Control.- 2.1 Model Following.- 2.2 Output Model Following.- 2.3 Stability and Positivity Concepts.- 2.4 Adaptive Control Based on CGT.- 2.5 The Adaptive Algorithm with General Input Commands 63 2.5.1 Controller Structure.- 2.6 Summary of Adaptive Algorithms.- 2A Proof of Theorem 2.1.- 2B Proof of Theorem 2.2.- 2C Poles, Zeros and Relative Degree in Multivariable Systems.- 3 Extensions of the Basic Adaptive Algorithm.- 3.1 Parallel Feedforward and Stability Considerations.- 3.2 Feedforward Around Plant.- 3.3 Feedforward in Both Plant and Model.- 3.4 A Unified Approach to Supplementary Dynamics.- 3.5 Adaptive Control in the Presence of Nonlinearities.- 3.6 Summary.- 3A Proof of Positivity Lemmas.- 3B Proof of Theorem 3.1.- 3C Proof of Theorem 3.2.- 3D Proof of Theorem 3.3.- 3E Proof of Theorem 3.4.- 4 Robust Design Procedures.- 4.1 Introduction.- 4.2 Robust Redesign of the Basic Adaptive Algorithm.- 4.3 Robustness Considerations with Feedforward in the Reference Model.- 4.4 Robust Redesign for Supplementary Dynamics.- 4.5 Bursting Phenomena and Their Elimination.- 4.6 Summary.- 4A Proof of Robust Stability, Theorem 1.- 4B Development of Lyapunov Function Derivative.- 4C Proof of Theorem 2.- 5 Adaptive Control of Time-Varying and Nonlinear Systems.- 5.1 Introduction.- 5.2 Passivity and Almost Passivity of Nonstationary Systems.- 5.3 Adaptive Control of ASP Plants.- 5.4 The "Almost Passivity" Lemmas.- 5.5 Adaptive Control of Nonlinear Systems.- 5A Proof of Stability for the Algorithm (5.27)-(5.32).- 5B Strictly Causal Almost Passive Systems.- 5C Proof of Lemma 1.- 6Design of Model Reference Adaptive Controllers.- 6.1 Algorithm Overview.- 6.2 Constraint Satisfaction.- 6.3 Weight Selection.- 6.4 Reference Model Selection.- 6.5 Digital Implementation.- 6.6 Time Varying Commands.- 7 Case Studies.- 7.1 Direct Model Reference Adaptive Control of a PUMA Manipulator.- 7.2 Model Reference Adaptive Control of Large Structures.- 7.3 Adaptive Drug Delivery Control.- 7.4 Adaptive Control for a Relaxed Static Stability Aircraft.- References.
1 Introduction.- 1.1 Definition of the Problem.- 1.2 Prologue to Simple Adaptive Control.- 1.3 Background on Adaptive Control Algorithms.- 1.4 Objectives and Overview.- 2 Basic Theory of Simple Adaptive Control.- 2.1 Model Following.- 2.2 Output Model Following.- 2.3 Stability and Positivity Concepts.- 2.4 Adaptive Control Based on CGT.- 2.5 The Adaptive Algorithm with General Input Commands 63 2.5.1 Controller Structure.- 2.6 Summary of Adaptive Algorithms.- 2A Proof of Theorem 2.1.- 2B Proof of Theorem 2.2.- 2C Poles, Zeros and Relative Degree in Multivariable Systems.- 3 Extensions of the Basic Adaptive Algorithm.- 3.1 Parallel Feedforward and Stability Considerations.- 3.2 Feedforward Around Plant.- 3.3 Feedforward in Both Plant and Model.- 3.4 A Unified Approach to Supplementary Dynamics.- 3.5 Adaptive Control in the Presence of Nonlinearities.- 3.6 Summary.- 3A Proof of Positivity Lemmas.- 3B Proof of Theorem 3.1.- 3C Proof of Theorem 3.2.- 3D Proof of Theorem 3.3.- 3E Proof of Theorem 3.4.- 4 Robust Design Procedures.- 4.1 Introduction.- 4.2 Robust Redesign of the Basic Adaptive Algorithm.- 4.3 Robustness Considerations with Feedforward in the Reference Model.- 4.4 Robust Redesign for Supplementary Dynamics.- 4.5 Bursting Phenomena and Their Elimination.- 4.6 Summary.- 4A Proof of Robust Stability, Theorem 1.- 4B Development of Lyapunov Function Derivative.- 4C Proof of Theorem 2.- 5 Adaptive Control of Time-Varying and Nonlinear Systems.- 5.1 Introduction.- 5.2 Passivity and Almost Passivity of Nonstationary Systems.- 5.3 Adaptive Control of ASP Plants.- 5.4 The "Almost Passivity" Lemmas.- 5.5 Adaptive Control of Nonlinear Systems.- 5A Proof of Stability for the Algorithm (5.27)-(5.32).- 5B Strictly Causal Almost Passive Systems.- 5C Proof of Lemma 1.- 6Design of Model Reference Adaptive Controllers.- 6.1 Algorithm Overview.- 6.2 Constraint Satisfaction.- 6.3 Weight Selection.- 6.4 Reference Model Selection.- 6.5 Digital Implementation.- 6.6 Time Varying Commands.- 7 Case Studies.- 7.1 Direct Model Reference Adaptive Control of a PUMA Manipulator.- 7.2 Model Reference Adaptive Control of Large Structures.- 7.3 Adaptive Drug Delivery Control.- 7.4 Adaptive Control for a Relaxed Static Stability Aircraft.- References.
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