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机械系统RBF神经网络控制 设计、分析及MATLAB仿真 英文PDF|Epub|txt|kindle电子书版本网盘下载

机械系统RBF神经网络控制 设计、分析及MATLAB仿真 英文
  • 刘金琨著 著
  • 出版社： 北京：清华大学出版社
  • ISBN：9787302302551
  • 出版时间：2013
  • 标注页数：365页
  • 文件大小：9MB
  • 文件页数：378页
  • 主题词：神经网络－应用－机械系统－英文

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图书目录

1 Introduction1

1.1 Neural Network Control1

1.1.1 Why Neural Network Control?1

1.1.2 Review of Neural Network Control2

1.1.3 Review of RBF Adaptive Control3

1.2 Review of RBF Neural Network3

1.3 RBF Adaptive Control for Robot Manipulators4

1.4 S Function Design for Control System5

1.4.1 S Function Introduction5

1.4.2 Basic Parameters in S Function5

1.4.3 Examples6

1.5 An Example of a Simple Adaptive Control System7

1.5.1 System Description7

1.5.2 Adaptive Control Law Design7

1.5.3 Simulation Example9

Appendix11

References15

2 RBF Neural Network Design and Simulation19

2.1 RBF Neural Network Design and Simulation19

2.1.1 RBF Algorithm19

2.1.2 RBF Design Example with Matlab Simulation20

2.2 RBF Neural Network Approximation Based on Gradient Descent Method22

2.2.1 RBF Neural Network Approximation22

2.2.2 Simulation Example24

2.3 Effect of Gaussian Function Parameters on RBF Approximation25

2.4 Effect of Hidden Nets Number on RBF Approximation28

2.5 RBF Neural Network Training for System Modeling33

2.5.1 RBF Neural Network Training33

2.5.2 Simulation Example34

2.6 RBF Neural Network Approximation36

Appendix37

References53

3 RBF Neural Network Control Based on Gradient Descent Algorithm55

3.1 Supervisory Control Based on RBF Neural Network55

3.1.1 RBF Supervisory Control55

3.1.2 Simulation Example56

3.2 RBFNN-Based Model Reference Adaptive Control58

3.2.1 Controller Design58

3.2.2 Simulation Example59

3.3 RBF Self-Adjust Control61

3.3.1 System Description61

3.3.2 RBF Controller Design61

3.3.3 Simulation Example63

Appendix63

References69

4 Adaptive RBF Neural Network Control71

4.1 Adaptive Control Based on Neural Approximation71

4.1.1 Problem Description71

4.1.2 Adaptive RBF Controller Design72

4.1.3 Simulation Examples75

4.2 Adaptive Control Based on Neural Approximation with Unknown Parameter79

4.2.1 Problem Description79

4.2.2 Adaptive Controller Design79

4.2.3 Simulation Examples83

4.3 A Direct Method for Robust Adaptive Control by RBF83

4.3.1 System Description83

4.3.2 Desired Feedback Control and Function Approximation86

4.3.3 Controller Design and Performance Analysis87

4.3.4 Simulation Example90

Appendix92

References112

5 Neural Network Sliding Mode Control113

5.1 Typical Sliding Mode Controller Design114

5.2 Sliding Mode Control Based on RBF for Second-Order SISO Nonlinear System116

5.2.1 Problem Description116

5.2.2 Sliding Mode Control Based on RBF for Unknown f(·)117

5.2.3 Simulation Example118

5.3 Sliding Mode Control Based on RBF for Unknown f(·) and g(·)120

5.3.1 Introduction120

5.3.2 Simulation Example122

Appendix123

References132

6 Adaptive RBF Control Based on Global Approximation133

6.1 Adaptive Control with RBF Neural Network Compensation for Robotic Manipulators134

6.1.1 Problem Description134

6.1.2 RBF Approximation135

6.1.3 RBF Controller and Adaptive Law Design and Analysis136

6.1.4 Simulation Examples140

6.2 RBF Neural Robot Controller Design with Sliding Mode Robust Term144

6.2.1 Problem Description144

6.2.2 RBF Approximation147

6.2.3 Control Law Design and Stability Analysis147

6.2.4 Simulation Examples148

6.3 Robust Control Based on RBF Neural Network with HJI153

6.3.1 Foundation153

6.3.2 Controller Design and Analysis153

6.3.3 Simulation Examples156

Appendix159

References191

7 Adaptive Robust RBF Control Based on Local Approximation193

7.1 Robust Control Based on Nominal Model for Robotic Manipulators193

7.1.1 Problem Description193

7.1.2 Controller Design194

7.1.3 Stability Analysis195

7.1.4 Simulation Example196

7.2 Adaptive RBF Control Based on Local Model Approximation for Robotic Manipulators197

7.2.1 Problem Description197

7.2.2 Controller Design199

7.2.3 Stability Analysis200

7.2.4 Simulation Examples203

7.3 Adaptive Neural Network Control of Robot Manipulators in Task Space205

7.3.1 Coordination Transformation from Task Space to Joint Space208

7.3.2 Neural Network Modeling of Robot Manipulators208

7.3.3 Controller Design210

7.3.4 Simulation Examples213

Appendix217

References249

8 Backstepping Control with RBF251

8.1 Introduction251

8.2 Backstepping Control for Inverted Pendulum252

8.2.1 System Description253

8.2.2 Controller Design253

8.2.3 Simulation Example254

8.3 Backstepping Control Based on RBF for Inverted Pendulum255

8.3.1 System Description255

8.3.2 Backstepping Controller Design256

8.3.3 Adaptive Law Design257

8.3.4 Simulation Example259

8.4 Backstepping Control for Single-Link Flexible Joint Robot260

8.4.1 System Description260

8.4.2 Backstepping Controller Design262

8.5 Adaptive Backstepping Control with RBF for Single-Link Flexible Joint Robot265

8.5.1 Backstepping Controller Design with Function Estimation265

8.5.2 Backstepping Controller Design with RBF Approximation269

8.5.3 Simulation Examples272

Appendix276

References291

9 Digital RBF Neural Network Control293

9.1 Adaptive Runge-Kutta-Merson Method293

9.1.1 Introduction293

9.1.2 Simulation Example295

9.2 Digital Adaptive Control for SISO System295

9.2.1 Introduction295

9.2.2 Simulation Example297

9.3 Digital Adaptive RBF Control for Two-Link Manipulators298

9.3.1 Introduction298

9.3.2 Simulation Example299

Appendix299

References309

10 Discrete Neural Network Control311

10.1 Introduction311

10.2 Direct RBF Control for a Class of Discrete-Time Nonlinear System312

10.2.1 System Description312

10.2.2 Controller Design and Stability Analysis312

10.2.3 Simulation Examples316

10.3 Adaptive RBF Control for a Class of Discrete-Time Nonlinear System319

10.3.1 System Description319

10.3.2 Traditional Controller Design320

10.3.3 Adaptive Neural Network Controller Design320

10.3.4 Stability Analysis322

10.3.5 Simulation Examples324

Appendix329

References337

11 Adaptive RBF Observer Design and Sliding Mode Control339

11.1 Adaptive RBF Observer Design339

11.1.1 System Description339

11.1.2 Adaptive RBF Observer Design and Analysis340

11.1.3 Simulation Examples343

11.2 Sliding Mode Control Based on RBF Adaptive Observer347

11.2.1 Sliding Mode Controller Design347

11.2.2 Simulation Example349

Appendix351

References362

Index363

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