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
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