基于神经网络的一类非线性系统自适应滑模控制

对一类非线性系统进行简化处理后,结合神经网络逼近方法、自适应滑模控制提出一种新的自适应控制方法.所设计的控制器分为两部分,一部分是等效控制器,另一部分是滑模控制器.滑模控制器用来减小系统的跟踪误差,起鲁棒控制作用.文中用神经网络逼近非线性函数,并将网络权值误差引入到神经网络权值的自适应律中用以改善系统的动态性能.最后给出的仿真算例证明所设计的控制器是十分有效的.

Adaptive Sliding Mode Control for a Class of Nonlinear System Based on Neural Networks

This paper is devoted to discussing a new control method which combine the approximation method of neural network with sliding mode control for a class of nonlinear systems. The controller is made up of an equivalent controller and an adaptive sliding mode controller. The sliding mode controller is a robust controller and it is used to diminish the track error of the control system. The neural networks are used to approximate the nonlinear functions and the approximation errors of the neural networks are introduced to the adaptive law in order to improve the quality of this system. Finally, an example is given to demonstrate the availability of this method.