刘金琨所著的《机械系统RBF神经网络控制(设计分析及Matlab仿真)》从matlab仿真角度，结合典型机械系统控制的实例，系统地介绍了神经网络控制的基本理论、基本方法和应用技术，是作者多年来从事控制系统教学和科研工作的结晶，同时融入了国内外同行近年来所取得的新成果。各部分内容既相互联系又相互独立。本书适用于从事生产过程自动化、计算机应用、机械电子和电气自动化领域工作的工程技术人员阅读，也可作为大专院校工业自动化、自动控制、机械电子、自动化仪表、计算机应用等专业的教学参考书。

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

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

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

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

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

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

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

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 ro

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

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

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

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