單控制器與多控制器自適應(yīng)動態(tài)規(guī)劃（英文版）內(nèi)容簡介

本書系統(tǒng)地介紹了基于自適應(yīng)學(xué)習(xí)控制的單控制器與多控制器系統(tǒng)最優(yōu)控制方法。分別針對單控制器系統(tǒng)與多控制器系統(tǒng)，提出了一系列自適應(yīng)動態(tài)規(guī)劃方法獲得系統(tǒng)的最優(yōu)控制策略。全書共分為11章，第1、2章主要講述自適應(yīng)動態(tài)規(guī)劃方法的基本原理以及實現(xiàn)方式。第3~7章主要介紹單控制器非線性系統(tǒng)迭代自適應(yīng)動態(tài)規(guī)劃最優(yōu)控制方法以及性能分析方案。第9~11章主要介紹多控制器系統(tǒng)迭代自適應(yīng)動態(tài)規(guī)劃最優(yōu)控制方法以及性能分析方案。

Contents

1 Introduction 1

1.1 Optimal Control 1

1.1.1 Continuous-Time LQR 1

1.1.2 Discrete-Time LQR 2

1.2 Adaptive Dynamic Programming 3

1.3 Review of Matrix Algebra 5

References 6

2 Neural-Network-BasedApproach for Finite-TimeOptimal Control 7

2.1 Introduction 7

2.2 Problem Formulation and Motivation 9

2.3 The Data-Based Identifier 9

2.4 Derivation of the Iterative ADP Algorithm with Convergence Analysis 11

2.5 Neural Network Implementation of theIterative Control Algorithm 17

2.6 Simulation Study 18

2.7 Conclusion 20

References 22

3 Nearly Finite-HorizonOptimalControlfor Nonafiine Time-Delay Nonlinear Systems 25

3.1 Introduction 25

3.2 Problem Statement 26

3.3 The Iteration ADP Algorithm and ItsConvergence 30

3.3.1 The Novel ADP Iteration Algorithm 30

3.3.2 Convergence Analysis of the Improved Iteration Algorithm 33

3.3.3 Neural Network Implementation of the Iteration ADP Algorithm 38

3.4 Simulation Study 40

3.5 Conclusion 48

References 48

4 Multi-objective Optimal Control for Time-Delay Systems 49

4.1 Introduction 49

4.2 Problem Formulation 50

4.3 Derivation of the ADP Algorithm for Time-Delay Systems 51

4.4 Neural Network Implementation for the Multi-objective Optimal Control Problem of Time-Delay Systems 54

4.5 Simulation Study 55

4.6 Conclusion 61

References 62

5 Multiple Actor-Critic Optimal Control via ADP 63

5.1 Introduction 63

5.2 Problem Statement 65

5.3 SIANN Architecture-Based Classification 66

5.4 Optimal Control Based on ADP 69

5.4.1 Model Neural Network 70

5.4.2 Critic Network and Action Network 74

5.5 Simulation Study 82

5.6 Conclusion 91

References 91

6 Optimal Control for a Class of Complex-Valued Nonlinear Systems 95

6.1 Introduction 95

6.2 Motivations and Preliminaries 96

6.3 ADP-Based Optimal Control Design 99

6.3.1 Critic Network 99

6.3.2 Action Network. 101

6.3.3 Design of the Compensation Controller 102

6.3.4 Stability Analysis 103

6.4 Simulation Study 107

6.5 Conclusion. 110

References 110

7 Off-Policy Neuro-Optimal Control for Unknown Complex-Valued Nonlinear Systems 113

7.1 Introduction 113

7.2 Problem Statement 114

7.3 Off-Policy Optimal Control Method 115

7.3.1 Convergence Analysis of Off-Policy PI Algorithm 117

7.3.2 Implementation Method of Off-Policy Iteration Algorithm 119

7.3.3 Implementation Process 122

7.4 Simulation Study 122

7.5 Conclusion 125

References 125

8 Approximation-Error-ADP-Based Optimal Tracking Control for Chaotic Systems 127

8.1 Introduction 127

8.2 Problem Formulation and Preliminaries 128

8.3 Optimal Tracking Control Scheme Basedon Approximation-Error ADP Algorithm 130

8.3.1 Description of Approximation-Error ADP Algorithm 130

8.3.2 Convergence Analysis of the Iterative ADP Algorithm 132

8.4 Simulation Study 136

8.5 Conclusion 144

References 144

9 Off-Policy Actor-Critic Structure for Optimal Controlof Unknown Systems with Disturbances 147

9.1 Introduction 147

9.2 Problem Statement 148

9.3 Off-Policy Actor-Critic Integral Reinforcement Learning 151

9.3.1 On-Policy IRL for Nonzero Disturbance 151

9.3.2 Off-Policy IRL for Nonzero Disturbance 152

9.3.3 NN Approximation for Actor-Critic Structure 154

9.4 Disturbance Compensation Redesign andStability Analysis 157

9.4.1 Disturbance Compensation Off-Policy Controller Design 157

9.4.2 Stability Analysis 158

9.5 Simulation Study 161

9.6 Conclusion 163

References 163

10 An Iterative ADP Method to Solve for a Class of Nonlinear Zero-Sum DifferentialGames 165

10.1 Introduction 165

10.2 Preliminaries and Assumptions 166

10.3 Iterative Approximate Dynamic Programming Method for ZS Differential Games 169

10.3.1 Derivation of the Iterative ADP Method 169

10.3.2 The Procedure of theMethod 174

10.3.3 The Properties of theIterativeADP Method 176

10.4 Neural Network Implementation 190

10.4.1 The Model Network 191

10.4.2 The Critic Network 192

10.4.3 The Action Network 193

10.5 Simulation Study 195

10.6 Conclusion 204

References 204

11 Neural-Network-Based Synchronous Iteration Learning Method for Multi-player Zero-Sum Games 207

11.1 Introduction 207

11.2 Motivations and Preliminaries 208

11.3 Synchronous Solution of Multi-playerZSGames 213

11.3.1 Derivation of Off-Policy Algorithm 213

11.3.2 Implementation Method for Off-Policy Algorithm 214

11.3.3 Stability Analysis 218

11.4 Simulation Study 219

11.5 Conclusion 224

References 224

12 Off-Policy Integral Reinforcement Learning Method for Multi-player Non-Zero-Sum Games 227

12.1 Introduction 227

12.2 Problem Statement 228

12.3 Multi-player Learning PI SolutionforNZSGames 229

12.4 Off-Policy Integral ReinforcementLearningMethod 234

12.4.1 Derivation of Off-Policy Algorithm 234

12.4.2 Implementation Method for Off-Policy Algorith

JMB/C流量型控制器，采用先進(jìn)的微電腦芯片技術(shù)，專門用于水處理設(shè)備的流量型再生的控制。該控制器可用于由多閥或多路閥組成的多罐過濾、軟化、除鹽等系統(tǒng)的獨立控制，具有瞬時流量、剩余量和累積水量顯示、再生程序預(yù)設(shè)定、系統(tǒng)運行狀態(tài)顯示與其他控制設(shè)備連鎖控制等特殊功能。

控制器性能特性

1.l 設(shè)置密碼保護(hù)

2.l JMB單通道或JMC1-3路多通道流量計脈沖接收

3.l 流量計探頭K值選擇（根據(jù)探頭安裝管道尺寸按附表查詢）

4.l 系統(tǒng)流量顯示（0-999t/h）及預(yù)先設(shè)定批量（1-9999.9t）的剩余量遞減顯示

5.l 累計流量查詢及清零功能

6.l 各路系統(tǒng)（工作、再生、備用、反饋）狀態(tài)顯示

7.l 本地強(qiáng)制手動啟動再生

8.l 再生啟動信號輸出時間（2-9999秒）設(shè)定

9.l 系統(tǒng)工作模式：JMC（C1、D2、D3、E2、E3）五種選擇

JMB（C1、C2、C3、C4、C5、D2、D3、D4、D5）九種選擇

注：C型：同時運行輪流再生；D型：多用一備；E型：同時運行分別再生

1.l 再生系統(tǒng)（出水電磁閥控制）輔助接點輸出

2.l 停電保護(hù)功能：停電后設(shè)定的參數(shù)保持時間大于3年

3.l 儀表外殼采用工程塑料，具有防塵、防腐、防水濺射等功能

4.l 儀表采用墻壁掛裝或支架懸掛安裝形式

主要技術(shù)指標(biāo)

1.l 流量傳感器脈沖量輸入：正弦波頻率范圍0-10K，探頭（直流）電壓幅度4-12V

2.l 采樣周期：0.2秒

3.l 輸出形式：繼電器無源接點輸出 觸點容量，AC220V/2A

4.l 饋電輸入：無源接點反饋信號

5.l 顯示方式：背光液晶顯示單元，使用壽命≥10年

6.l 儀表電源：開關(guān)電源 100-265V/AC 功耗5W

7.l 使用環(huán)境：環(huán)境溫度4-60℃ 相對濕度 ≤85%RH

8.l 控制器外殼尺寸：260X170X105mm

JMA--JMC連接示意圖

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