非线性系统自学习最优控制
全新正版 极速发货
￥ 76.85 6.4折 ￥ 120 全新
库存3件
广东广州
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作者Qinglai Wei,Ruizhuo Song,Benkai Li 等
出版社科学出版社
ISBN9787030520609
出版时间2018-01
装帧精装
开本其他
定价120元
货号1201629801
上书时间2024-09-06
商品详情
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目录
1 Principle of Adaptive Dynamic Programming 1
1.1 Dynamic Programming 1
1.1.1 Discrete-Time Systems 1
1.1.2 Continuous-Time Systems 2
1.2 Original Forms of Adaptive Dynamic Programming 3
1.2.1 Principle of Adaptive Dynamic Programming 4
1.3 Iterative Forms of Adaptive Dynamic Programming 9
1.3.1 Value Iteration 9
1.3.2 Policy Iteration 10
1.4 About This Book 11
References 14
2 An Iterative *-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems with Unfixed Initial State 19
2.1 Introduction 19
2.2 Problem Statement 20
2.3 Properties of the Iterative Adaptive Dynamic Programming Algorithm 21
2.3.1 Derivation of the Iterative ADP Algorithm 21
2.3.2 Properties of the Iterative ADP Algorithm 23
2.4 The *-Optimal Control Algorithm 28
2.4.1 The Derivation of the *-Optimal Control Algorithm 28
2.4.2 Properties of the *-Optimal Control Algorithm 32
2.4.3 The *-Optimal Control Algorithm for Unfixed Initial State 34
2.4.4 The Expressions of the *-Optimal Control Algorithm 37
2.5 Neural Network Implementation for the *-Optimal Control Scheme 37
2.5.1 The Critic Network 38
2.5.2 The Action Network 39
2.6 Simulation Study 40
2.7 Conclusions 42
References 43
3 Discrete-Time Optimal Control of Nonlinear Systems via Value Iteration-Based Q-Learning 47
3.1 Introduction 47
3.2 Preliminaries and Assumptions 49
3.2.1 Problem Formulations 49
3.2.2 Derivation of the Discrete-Time Q-Learning Algorithm 50
3.3 Properties of the Discrete-Time Q-Learning Algorithm 52
3.3.1 Non-Discount Case 52
3.3.2 Discount Case 59
3.4 Neural Network Implementation for the Discrete-Time Q-Learning Algorithm 64
3.4.1 The Action Network 65
3.4.2 The Critic Network 67
3.4.3 Training Phase 69
3.5 Simulation Study 70
3.5.1 Example 1 70
3.5.2 Example 2 76
3.6 Conclusion 81
References 82
4 A Novel Policy Iteration-Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems 85
4.1 Introduction 85
4.2 Problem Formulation 86
4.3 Policy Iteration-Based Deterministic Q-Learning Algorithm for Discrete-Time Nonlinear Systems 87
4.3.1 Derivation of the Policy Iteration-Based Deterministic Q-Learning Algorithm 87
4.3.2 Properties of the Policy Iteration-Based Deterministic Q-Learning Algorithm 89
4.4 Neural Network Implementation for the Policy Iteration-Based Deterministic Q-Learning Algorithm 93
4.4.1 The Critic Network 93
4.4.2 The Action Network 95
4.4.3 Summary of the Policy Iteration-Based Deterministic Q-Learning Algorithm 96
4.5 Simulation Study 97
4.5.1 Example 1 97
4.5.2 Example 2 100
4.6 Conclusion 107
References 107
5 Nonlinear Neuro-Optimal Tracking Control via Stable Iterative Q-Learning AIgorithm 111
5.1 lntroduction 111
5.2 Problem Statement 112
5.3 Policy Iteration Q-Leaming Algotithm for Optimal Tracking Control 114
5.4 Properties of the Policy Iteration Q-Learning Algorithm 114
5.5 Neural Network Implementation for the Policy Iteration Q-Leaming Algorithm 119
5.5.1 The Critic Network 120
5.5.2 The Action Network 120
5.6 Simulation Study 121
5.6.1 Example 1 122
5.6.2 Example 2 125
5.7 Conclusions 129
References 129
6 Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions 133
6.1 Introduction 133
6.2 Preliminaries 134
6.3 Multiobjective Adaptive Dynamic Programming Method 135
6.4 Model-Free Incremental Q-Leaming Method 145
6.5 Neural Network Implementation for the Incremental Q-Learning Method 147
6.5.1 The Critic Network 148
6.5.2 The Action Network 149
6.5.3 The Procedure of the Model-Free Incremental Q-Iearning Method 150
6.6 Convergence Proof 150
6.7 Simulation Study 153
6.7.1 Example 1 153
6.7.2 Example 2 155
6.8 Conclusion 157
References 157
7 Multiobjective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm 159
7.1 Introduction 159
7.2 General Formulation 160
7.3 Optimal Solution Based on Finite-Approximation-Error ADP 162
7.3.1 Data-Based Identifier of Unknown System Dynamics 162
7.3.2 Derivation of the ADP Algorithm with Finite Approximation Errors 166
7.3.3 Convergence Analysis of the Iterative ADP Algorithm 168
7.4 Implementation of the Iterative ADP Algorithm 173
7.4.1 Critic Network 174
7.4.2 The Action Network 174
7.4.3 The Procedure of the ADP Algorithm 175
7.5 Simulation Study 175
7.5.1 Example 1 176
7.5.2 Example 2 179
7.6 Conclusions 182
References 182
8 A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm 185
8.1 Introduction 185
8.2 Problem Statement 185
8.3 Optimal Control Based on Online ADP Algorithm 187
8.3.1 Design Method of the Critic Network and the Action Network 188
8.3.2 Stability Anal
内容摘要
《非线性系统自学习很优控制：自适应动态规划方法（英文版）》presents a class of novel， self-learning， optimal control schemes based on adaptive dynamic programming techniques， which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods， including the convergence of the iterative value functions and the stability of the system under iterative control laws， helping to guarantee the effectiveness of the methods developed. When the system model is known， self-learning optimal control is designed on the basis of the system model; when the system model is not known， adaptive dynamic programming is implemented according to the system data， effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis， the book is a valuable guide for engineers， researchers， and students in control science and engineering.
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