基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.

Adaptive H∞ control of a class of uncertain nonlinear systems based on RBF neural networks

An adaptive H-infinity control method based on RBF neural network is proposed for uncertain nonlinear systems. The controller consists of an equivalent controller and an H-infinity controller. The RBF 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 the systems. H-infinity controller is designed to attenuate external disturbance and approximation errors of the neural networks. The controller can guarantee stability of the overall system and attenuate the effect of the external disturbance and approximation errors of the neural networks to a prescribed level. Finally, an example is given to illustrate the availability of this method.