Advanced Adaptive Control
Advanced Adaptive Control
Index: 1. Introduction 1.1 Introduction 1.2 Adaptive Control Schemes 1.3 The Context of Adaptive Control 1.4 Book Outline 2. Preliminaries 2.1 Matrices 2.2 Eigenvalues, Eigenvectors and Eigenrows 2.3 Time-Varying Matrices 2.4 Norms and Inner Products 2.5 Dynamic System Representations 2.6 Least Squares Estimation 2.7 Technical Estimation 2.8 A Design Example 3. Artificial Neural Networks: Aspects of Modelling and Learning 3.1 Introduction 3.2 Network Approximation 3.3 Multi-Layer Perceptrons 3.4 Associative Memory Networks 3.5 Polynomial Neural Networks 3.6 The Curse of Dimensionality 3.7 Supervised Learning 3.8 Instantaneous Learning Rules 3.9 Weight Convergence 3.10 A Geometric Interpretation of the LMS Algorithm 3.11 Gradient Noise 4. Fuzzy Modelling and Control Systems 4.1 Fuzzy Systems 4.2 Neurofuzzy Systems 4.3 Adaptive Fuzzy Models 4.4 Adaptive Fuzzy Control Systems 5. Neural Network and Fuzzy Logic Based Adaptive Control 5.1 Introduction 5.2 Matching Systems 5.3 Mismatching Systems 5.4 General Systems 5.5 Fuzzy Logic Based Control 5.6 Conclusions 6. Sup Controllers and Self-Tuning Sup Regulators 6.1 Introduction 6.2 External Input Spaces 6.3 Performance Index 6.4 Sup Regulators and Output Performance 6.5 Sup Controllers and Output Performance 6.6 Sup Controllers for Non-Minimum Phase Plants 6.7 The Self-Tuning Sup Regulator Algorithm 6.8 Convergence of the Self-Tuning Algorithm 6.9 An Example 6.10 Conclusions 7. Mean Controllers and Self-Tuning Mean Regulators 7.1 Introduction 7.2 Input Spaces and Performance Index 7.3 Mean Regulators and Output Performance 7.4 Mean Controllers and Output Performance 7.5 Stability of the Closed Loop Systems 7.6 The Self-Tuning Mean Regulator Algorithm 7.7 Conclusions 8. MRAPC for Time Delay Systems 8.1 Introduction 8.2 The Smith Predictive Control 8.3 The Modified Smith Predictor Control 8.4 MRAPC Using Parametric Optimization Theory 8.5 MRAPC Using Lyapunov Stability Theory 8.6 Conclusions 9. Rule-based Adaptive Control Systems Design 9.1 Introduction 9.2 Systems Representation 9.3 Meta Identification Rules 9.4 Model Adjusting Rules 9.5 Controller Tuning via Rule Based Method 9.6 Simulation and Application 9.7 Conclusions 10. Adaptive Control of Singular Systems 10.1 Introduction 10.2 System Representation 10.3 Parameter Estimation 10.4 Preliminary Feedback Design 10.5 Adaptive Control Design 10.6 Simulation Results 10.7 Application to The Control of A Gas Turbine 10.8 Conclusions Contents
Wang, H.
d23f04f1-a300-4744-bd98-2df77c7047df
Liu, G.P.
abd13432-6fa2-4301-884d-7083ec4e7197
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
1995
Wang, H.
d23f04f1-a300-4744-bd98-2df77c7047df
Liu, G.P.
abd13432-6fa2-4301-884d-7083ec4e7197
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Wang, H., Liu, G.P., Harris, C.J. and Brown, M.
(1995)
Advanced Adaptive Control
,
Pergamon Press
Abstract
Index: 1. Introduction 1.1 Introduction 1.2 Adaptive Control Schemes 1.3 The Context of Adaptive Control 1.4 Book Outline 2. Preliminaries 2.1 Matrices 2.2 Eigenvalues, Eigenvectors and Eigenrows 2.3 Time-Varying Matrices 2.4 Norms and Inner Products 2.5 Dynamic System Representations 2.6 Least Squares Estimation 2.7 Technical Estimation 2.8 A Design Example 3. Artificial Neural Networks: Aspects of Modelling and Learning 3.1 Introduction 3.2 Network Approximation 3.3 Multi-Layer Perceptrons 3.4 Associative Memory Networks 3.5 Polynomial Neural Networks 3.6 The Curse of Dimensionality 3.7 Supervised Learning 3.8 Instantaneous Learning Rules 3.9 Weight Convergence 3.10 A Geometric Interpretation of the LMS Algorithm 3.11 Gradient Noise 4. Fuzzy Modelling and Control Systems 4.1 Fuzzy Systems 4.2 Neurofuzzy Systems 4.3 Adaptive Fuzzy Models 4.4 Adaptive Fuzzy Control Systems 5. Neural Network and Fuzzy Logic Based Adaptive Control 5.1 Introduction 5.2 Matching Systems 5.3 Mismatching Systems 5.4 General Systems 5.5 Fuzzy Logic Based Control 5.6 Conclusions 6. Sup Controllers and Self-Tuning Sup Regulators 6.1 Introduction 6.2 External Input Spaces 6.3 Performance Index 6.4 Sup Regulators and Output Performance 6.5 Sup Controllers and Output Performance 6.6 Sup Controllers for Non-Minimum Phase Plants 6.7 The Self-Tuning Sup Regulator Algorithm 6.8 Convergence of the Self-Tuning Algorithm 6.9 An Example 6.10 Conclusions 7. Mean Controllers and Self-Tuning Mean Regulators 7.1 Introduction 7.2 Input Spaces and Performance Index 7.3 Mean Regulators and Output Performance 7.4 Mean Controllers and Output Performance 7.5 Stability of the Closed Loop Systems 7.6 The Self-Tuning Mean Regulator Algorithm 7.7 Conclusions 8. MRAPC for Time Delay Systems 8.1 Introduction 8.2 The Smith Predictive Control 8.3 The Modified Smith Predictor Control 8.4 MRAPC Using Parametric Optimization Theory 8.5 MRAPC Using Lyapunov Stability Theory 8.6 Conclusions 9. Rule-based Adaptive Control Systems Design 9.1 Introduction 9.2 Systems Representation 9.3 Meta Identification Rules 9.4 Model Adjusting Rules 9.5 Controller Tuning via Rule Based Method 9.6 Simulation and Application 9.7 Conclusions 10. Adaptive Control of Singular Systems 10.1 Introduction 10.2 System Representation 10.3 Parameter Estimation 10.4 Preliminary Feedback Design 10.5 Adaptive Control Design 10.6 Simulation Results 10.7 Application to The Control of A Gas Turbine 10.8 Conclusions Contents
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Published date: 1995
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Southampton Wireless Group
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Local EPrints ID: 250122
URI: http://eprints.soton.ac.uk/id/eprint/250122
PURE UUID: b847c359-3764-4b69-bb24-2527889a8e98
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Author:
H. Wang
Author:
G.P. Liu
Author:
C.J. Harris
Author:
M. Brown
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