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Stable adaptive neural network control

Author: S S Ge
Publisher: Boston : Kluwer Academic, ©2002.
Series: Kluwer international series on Asian studies in computer and information science, 13.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:

A study of stable adaptive control designs using approximation-based techniques. It presents analysis for system stability and control performance. It also discusses linearly parameterized and  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: S S Ge
ISBN: 0792375971 9780792375975
OCLC Number: 48038244
Description: xvi, 282 pages : illustrations ; 24 cm.
Contents: Machine generated contents note: 1 Introduction 1 --
1.1 Introduction1 --
1.2 Adaptive Control2 --
1.3 Neural Network Control 3 3 --
1.4 Instability Mechanisms in Adaptive Neural Control Systems 5 --
1.5 Outline of the Book9 --
1.6 Conclusion 10 --
2 Mathematical Preliminaries 11 --
2.1 Introduction 11 --
2.2 Mathematical Preliminaries11 --
2.2.1 Norms for Vectors and Signals12 --
2.2.2 Properties of Matrix15 --
2.3 Concepts of Stability16 --
2.4 Lyapunov Stability Theorem17 --
2.5 Useful Theorems and Formula19 --
2.5.1 Sliding Surface19 --
2.5.2 Mean Value Theorem20 --
2.5.3 Integral Formula20 --
2.5.4 Implicit Function Theorem23 --
2.5.5 Input-Output Stability25 --
2.6 Conclusion26 --
3 Neural Networks and Function Approximation 27 --
3.1 Introduction. 27 --
3.2 Function Approximation27 --
3.3 Linearly Parametrized Neural Networks29 --
3.4 Non-linearly Parametrized Networks35 --
3.5 Neural Networks for Control Applications44 --
3.6 Conclusion46 --
4 SISO Nonlinear Systems 47 --
4.1 Introduction47 --
4.2 NN Control with Regional Stability49 --
4.2.1 Desired Feedback Control49 --
4.2.2 HONN Controller Design Based on (4.7)51 --
4.2.3 MNN Control Based on (4.10)59 --
4.3 VSC --
Semi-Global Stability70 --
4.3.1 VSC-based Adaptive NN Control Design73 --
4.3.2 Elimination for Controller Chattering77 --
4.3.3 Simulation Study79 --
4.4 Conclusion:79 --
5 ILF for Adaptive Control 81 --
5.1 Introduction 81 --
5.2 Matching SISO Nonlinear Systems82 --
5.2.1 Integral Lyapunov Function83 --
5.2.2 Choice of Weighting Function a(x)83 --
5.2.3 Adaptive NN Control Based on DFCs92 --
5.3 Backstepping Adaptive NN Design105 --
5.3.1 Adaptive Design for a First-order System108 --
5.3.2 Design for nth-order Systems 112 --
5.3.3 Controller Design with Reduced Knowledge 121 --
5.3.4 Simulation Studies123 --
5.4 NN Control for MIMO Nonlinear Systems127 --
5.4.1 System Description128 --
5.4.2 Lyapunov Function Design and Control Structure. 130 --
5.4.3 Adaptive MIMO Control Using MNNs132 --
5.5 Conclusion138 --
6 Non-affine Nonlinear Systems 139 --
6.1 Introduction139 --
6.2 System Description and Properties140 --
6.2.1 Implicit Desired Feedback Control 142 --
6.2.2 High-gain Observer146 --
6.3 Controller Design Based on LPNN147 --
6.3.1 State Feedback Control 149 --
6.3.2 Output Feedback Control 153 --
6.3.3 Simulation Study159 --
6.4 Controller Design Based on MNN 160 --
6.4.1 State Feedback Control 163 --
6.4.2 Output Feedback Control 168 --
6.4.3 Application to CSTR176 --
6.5 Conclusion182 --
7 lTiangular Nonlinear Systems 183 --
7.1 Introduction183 --
7.2 Special Systems in Strict-Feedback Form185 --
7.2.1 Direct Adaptive NN Control188 --
7.2.2 Simulation studies198 --
7.3 Partially Known Nonlinear Systems201 --
7.3.1 Adaptive Neural Control Design203 --
7.3.2 Numerical Simulation215 --
7.4 Pure-feedback Nonlinear Systems217 --
7.4.1 Direct Adaptive NN Control for 1E 220 --
7.4.2 Direct Adaptive NN Control for E2235 --
7.4.3 Simulation studies240 --
7.5 MIMO Nonlinear Systems242 --
7.6 Conclusion260 --
8 Conclusion 261 --
8.1 Conclusion 261 --
8.2 Design Flexibility262 --
8.3 Further Research263 --
References 265 --
Index 281.
Series Title: Kluwer international series on Asian studies in computer and information science, 13.
Responsibility: by S.S. Ge [and others].
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