机械系统RBF神经网络控制
2013-3
清华大学出版社
刘金琨
《机械系统rbf神经网络控制：设计、分析及matlab仿真(英文版)(精)》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。
全书共分11章，包括rbf网络的设计及分析、基于梯度下降法的rbf网络控制、简单的rbf网络自适应控制，rbf网络滑模控制、基于rbf网络逼近的自适应控制、基于rbf网络的自适应反演控制、rbf网络数字控制、离散系统的rbf网络控制及自适应rbf网络观测器的设计。每种控制方法都通过matlab进行了仿真分析。
《机械系统rbf神经网络控制：设计、分析及matlab仿真(英文版)(精)》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 appendix2 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 appendix3 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 appendix4 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 appendix5 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 appendix6 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 appendix7 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 appendix8 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 appendix9 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 appendix10 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 appendix11 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 appendixindex
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。
内容挺丰富的！英文很正宗！