机械系统RBF神经网络控制：设计、分析及Matlab仿真（英文）

Contents Chapter 1 Introduction 1.1 Neural Network Control 1.1.1 Why Neural Network Control? 1.1.2 Review of Neural Network Control 1.1.3 Review of RBF Adaptive Control 1.2 Review of RBF Neural Network 1.3 RBF Adaptive Control for Robot Manipulators 1.4 S Function Design for Control System 1.4.1 S Function Introduction 1.4.2 Basic Parameters in S Function 1.4.3 Examples 1.5 An Example of a Simple Adaptive Control System 1.5.1 System Description 1.5.2 Adaptive Control Law Design 1.5.3 Simulation Example
References
Appendix Chapter 2 RBF Neural Network Design and Simulation 2.1 RBF Neural Network Design and Simulation 2.1.1 RBF Algorithm 2.1.2 RBF Design Example with Matlab Simulation 2.2  RBF Neural Network Approximation Based on Gradient Descent Method
2.2.1 RBF Neural Network Approximation 2.2.2 Simulation Example 2.3 Effect of Gaussian Function Parameters on RBF Approximation 2.4 Effect of Hidden Nets Number on RBF Approximation 2.5 RBF Neural Network Training for System Modeling 2.5.1 RBF Neural Network Training 2.5.2 Simulation Example 2.6 RBF Neural Network Approximation
References
Appendix Chapter 3 RBF Neural Network Control Based on Gradient Descent Algorithm
3.1  Supervisory Control Based on RBF Neural Network 3.1.1 RBF Supervisory Control 3.1.2 Simulation Example 3.2  RBFNN Based Model Reference Adaptive Control 3.2.1 Controller Design 3.2.2 Simulation Example 3.3  RBF Self-Adjust Control 3.3.1 System Description 3.3.2 RBF Controller Design 3.3.3 Simulation Example
References
Appendix Chapter 4 Adaptive RBF Neural Network Control 4.1  Adaptive Control Based on Neural Approximation 4.1.1 Problem Description 4.1.2 Adaptive RBF Controller Design 4.1.3 Simulation Examples 4.2  Adaptive Control Based on Neural Approximation with Unknown Parameter
4.2.1 Problem Description 4.2.2 Adaptive Controller Design 4.2.3 Simulation Examples 4.3  A Direct Method for Robust Adaptive Control by RBF 4.3.1 System Description 4.3.2 Desired Feedback Control and Function Approximation 4.3.3 Controller Design and Performance Analysis 4.3.4 Simulation Example
References
Appendix Chapter 5 Neural Network Sliding Mode Control 5.1  Typical Sliding Mode Controller Design 5.2  Sliding Mode Control Based on RBF for Second-Order SISO Nonlinear System
5.2.1 Problem Description 5.2.2 Sliding Mode Control Based on RBF for Unknown f().
5.2.3 Simulation Example 5.3  Sliding Mode Control Based on RBF for Unknown f(). and g().
5.3.1 Introduction 5.3.2 Simulation Example References
Appendix Chapter 6 Adaptive RBF Control Based on Global Approximation 6.1  Adaptive Control with RBF Neural Network Compensation for Robotic Manipulators
6.1.1  Problem Description 6.1.2  RBF Approximation 6.1.3  RBF Controller and Adaptive Law Design and Analysis 6.1.4  Simulation Examples 6.2  RBF Neural Robot Controller Design with Sliding Mode Robust Term
6.2.1  Problem Description 6.2.2  RBF Approximation 6.2.3  Control Law Design and Stability Analysis 6.2.4  Simulation Examples 6.3  Robust Control Based on RBF Neural Network with HJI 6.3.1  Foundation 6.3.2  Controller Design and Analysis 6.3.3 Simulation Examples
References
Appendix Chapter 7 Adaptive Robust RBF Control Based on Local Approximation
7.1  Robust Control Based on Nominal Model for Robotic Manipulators
7.1.1  Problem Description 7.1.2  Controller Design 7.1.3  Stability Analysis 7.1.4  Simulation Example 7.2  Adaptive RBF Control Based on Local Model Approximation for Robotic Manipulators
7.2.1  Problem Description 7.2.2  Controller Design 7.2.3  Stability Analysis 7.2.4  Simulation Examples 7.3  Adaptive Neural Network Control of Robot Manipulators in Task Space
7.3.1  Coordination Transformation from Task Space to Joint Space
7.3.2  Neural Network Modeling of Robot Manipulators 7.3.3  Controller Design 7.3.4 Simulation Examples
References
Appendix Chapter 8 Backstepping Control with RBF 8.1  Introduction 8.2  Backstepping Control for Inverted Pendulum 8.2.1  System Description 8.2.2  Controller Design 8.2.3  Simulation Example 8.3  Backstepping Control Based on RBF for Inverted Pendulum 8.3.1  System Description 8.3.2  Backstepping Controller Design 8.3.3  Adaptive Law Design 8.3.4  Simulation Example 8.4  Backstepping Control for Single Link Flexible Joint Robot 8.4.1  System Description 8.4.2  Backstepping Controller Design 8.5  Adaptive Backstepping Control with RBF for Single Link Flexible Joint Robot
8.5.1  Backstepping Controller Design with Function Estimation
8.5.2  Backstepping Controller Design with RBF Approximation
8.5.3 Simulation Examples
References
Appendix Chapter 9 Digital RBF Neural Network Control 9.1  Adaptive Runge-Kutta-Merson Method 9.1.1  Introduction 9.1.2  Simulation Example 9.2  Digital Adaptive Control for SISO System 9.2.1  Introduction 9.2.2  Simulation Example 9.3  Digital Adaptive RBF Control for Two Link Manipulators 9.3.1  Introduction 9.3.2 Simulation Example
References
Appendix Chapter 10 Discrete Neural Network Control 10.1  Introduction 10.2  Direct RBF Control for a Class of Discrete-time Nonlinear System
10.2.1 System Description 10.2.2 Controller Design and Stability Analysis 10.2.3 Simulation Examples 10.3  Adaptive RBF Control for a Class of Discrete-Time Nonlinear System
10.3.1 System Description 10.3.2 Traditional Controller Design 10.3.3 Adaptive Neural Network Controller Design 10.3.4 Stability Analysis 10.3.5 Simulation Examples
References
Appendix Chapter 11 Adaptive RBF Observer Design and Sliding Mode Control
11.1  Adaptive RBF observer design 11.1.1 System Description 11.1.2 Adaptive RBF Observer Design and Analysis 11.1.3 Simulation Examples 11.2  Sliding Mode Control Based on RBF Adaptive Observer 11.2.1 Sliding Mode Controller Design 11.2.2 Simulation Example
References
Appendix Index