机械系统RBF神经网络控制 设计、分析及MATLAB仿真 英文pdf电子书版本下载

点此进入-本书在线PDF格式电子书下载【推荐-云解压-方便快捷】直接下载PDF格式图书。移动端-PC端通用
下载压缩包 [复制下载地址] 温馨提示：（请使用BT下载软件FDM进行下载）软件下载地址页

机械系统RBF神经网络控制 设计、分析及MATLAB仿真 英文PDF格式电子书版下载

下载的文件为RAR压缩包。需要使用解压软件进行解压得到PDF格式图书。

建议使用BT下载工具Free Download Manager进行下载,简称FDM(免费,没有广告,支持多平台）。本站资源全部打包为BT种子。所以需要使用专业的BT下载软件进行下载。如 BitComet qBittorrent uTorrent等BT下载工具。迅雷目前由于本站不是热门资源。不推荐使用！后期资源热门了。安装了迅雷也可以迅雷进行下载！

（文件页数 要大于 标注页数，上中下等多册电子书除外）

注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具

1 Introduction 1

1.1 Neural Network Control 1

1.1.1 Why Neural Network Control? 1

1.1.2 Review of Neural Network Control 2

1.1.3 Review of RBF Adaptive Control 3

1.2 Review of RBF Neural Network 3

1.3 RBF Adaptive Control for Robot Manipulators 4

1.4 S Function Design for Control System 5

1.4.1 S Function Introduction 5

1.4.2 Basic Parameters in S Function 5

1.4.3 Examples 6

1.5 An Example of a Simple Adaptive Control System 7

1.5.1 System Description 7

1.5.2 Adaptive Control Law Design 7

1.5.3 Simulation Example 9

Appendix 11

References 15

2 RBF Neural Network Design and Simulation 19

2.1 RBF Neural Network Design and Simulation 19

2.1.1 RBF Algorithm 19

2.1.2 RBF Design Example with Matlab Simulation 20

2.2 RBF Neural Network Approximation Based on Gradient Descent Method 22

2.2.1 RBF Neural Network Approximation 22

2.2.2 Simulation Example 24

2.3 Effect of Gaussian Function Parameters on RBF Approximation 25

2.4 Effect of Hidden Nets Number on RBF Approximation 28

2.5 RBF Neural Network Training for System Modeling 33

2.5.1 RBF Neural Network Training 33

2.5.2 Simulation Example 34

2.6 RBF Neural Network Approximation 36

Appendix 37

References 53

3 RBF Neural Network Control Based on Gradient Descent Algorithm 55

3.1 Supervisory Control Based on RBF Neural Network 55

3.1.1 RBF Supervisory Control 55

3.1.2 Simulation Example 56

3.2 RBFNN-Based Model Reference Adaptive Control 58

3.2.1 Controller Design 58

3.2.2 Simulation Example 59

3.3 RBF Self-Adjust Control 61

3.3.1 System Description 61

3.3.2 RBF Controller Design 61

3.3.3 Simulation Example 63

Appendix 63

References 69

4 Adaptive RBF Neural Network Control 71

4.1 Adaptive Control Based on Neural Approximation 71

4.1.1 Problem Description 71

4.1.2 Adaptive RBF Controller Design 72

4.1.3 Simulation Examples 75

4.2 Adaptive Control Based on Neural Approximation with Unknown Parameter 79

4.2.1 Problem Description 79

4.2.2 Adaptive Controller Design 79

4.2.3 Simulation Examples 83

4.3 A Direct Method for Robust Adaptive Control by RBF 83

4.3.1 System Description 83

4.3.2 Desired Feedback Control and Function Approximation 86

4.3.3 Controller Design and Performance Analysis 87

4.3.4 Simulation Example 90

Appendix 92

References 112

5 Neural Network Sliding Mode Control 113

5.1 Typical Sliding Mode Controller Design 114

5.2 Sliding Mode Control Based on RBF for Second-Order SISO Nonlinear System 116

5.2.1 Problem Description 116

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

5.2.3 Simulation Example 118

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

5.3.1 Introduction 120

5.3.2 Simulation Example 122

Appendix 123

References 132

6 Adaptive RBF Control Based on Global Approximation 133

6.1 Adaptive Control with RBF Neural Network Compensation for Robotic Manipulators 134

6.1.1 Problem Description 134

6.1.2 RBF Approximation 135

6.1.3 RBF Controller and Adaptive Law Design and Analysis 136

6.1.4 Simulation Examples 140

6.2 RBF Neural Robot Controller Design with Sliding Mode Robust Term 144

6.2.1 Problem Description 144

6.2.2 RBF Approximation 147

6.2.3 Control Law Design and Stability Analysis 147

6.2.4 Simulation Examples 148

6.3 Robust Control Based on RBF Neural Network with HJI 153

6.3.1 Foundation 153

6.3.2 Controller Design and Analysis 153

6.3.3 Simulation Examples 156

Appendix 159

References 191

7 Adaptive Robust RBF Control Based on Local Approximation 193

7.1 Robust Control Based on Nominal Model for Robotic Manipulators 193

7.1.1 Problem Description 193

7.1.2 Controller Design 194

7.1.3 Stability Analysis 195

7.1.4 Simulation Example 196

7.2 Adaptive RBF Control Based on Local Model Approximation for Robotic Manipulators 197

7.2.1 Problem Description 197

7.2.2 Controller Design 199

7.2.3 Stability Analysis 200

7.2.4 Simulation Examples 203

7.3 Adaptive Neural Network Control of Robot Manipulators in Task Space 205

7.3.1 Coordination Transformation from Task Space to Joint Space 208

7.3.2 Neural Network Modeling of Robot Manipulators 208

7.3.3 Controller Design 210

7.3.4 Simulation Examples 213

Appendix 217

References 249

8 Backstepping Control with RBF 251

8.1 Introduction 251

8.2 Backstepping Control for Inverted Pendulum 252

8.2.1 System Description 253

8.2.2 Controller Design 253

8.2.3 Simulation Example 254

8.3 Backstepping Control Based on RBF for Inverted Pendulum 255

8.3.1 System Description 255

8.3.2 Backstepping Controller Design 256

8.3.3 Adaptive Law Design 257

8.3.4 Simulation Example 259

8.4 Backstepping Control for Single-Link Flexible Joint Robot 260

8.4.1 System Description 260

8.4.2 Backstepping Controller Design 262

8.5 Adaptive Backstepping Control with RBF for Single-Link Flexible Joint Robot 265

8.5.1 Backstepping Controller Design with Function Estimation 265

8.5.2 Backstepping Controller Design with RBF Approximation 269

8.5.3 Simulation Examples 272

Appendix 276

References 291

9 Digital RBF Neural Network Control 293

9.1 Adaptive Runge-Kutta-Merson Method 293

9.1.1 Introduction 293

9.1.2 Simulation Example 295

9.2 Digital Adaptive Control for SISO System 295

9.2.1 Introduction 295

9.2.2 Simulation Example 297

9.3 Digital Adaptive RBF Control for Two-Link Manipulators 298

9.3.1 Introduction 298

9.3.2 Simulation Example 299

Appendix 299

References 309

10 Discrete Neural Network Control 311

10.1 Introduction 311

10.2 Direct RBF Control for a Class of Discrete-Time Nonlinear System 312

10.2.1 System Description 312

10.2.2 Controller Design and Stability Analysis 312

10.2.3 Simulation Examples 316

10.3 Adaptive RBF Control for a Class of Discrete-Time Nonlinear System 319

10.3.1 System Description 319

10.3.2 Traditional Controller Design 320

10.3.3 Adaptive Neural Network Controller Design 320

10.3.4 Stability Analysis 322

10.3.5 Simulation Examples 324

Appendix 329

References 337

11 Adaptive RBF Observer Design and Sliding Mode Control 339

11.1 Adaptive RBF Observer Design 339

11.1.1 System Description 339

11.1.2 Adaptive RBF Observer Design and Analysis 340

11.1.3 Simulation Examples 343

11.2 Sliding Mode Control Based on RBF Adaptive Observer 347

11.2.1 Sliding Mode Controller Design 347

11.2.2 Simulation Example 349

Appendix 351

References 362

Index 363