【作者】 许博； 李树荣；

【Author】 Bo Xu,Shurong Li School of Information and Control Engineering,China University of Petroleum,Dongying,Shandong 257061,P.R.China

【机构】 中国石油大学(华东)信息与控制工程学院；

【摘要】 在实际的系统中,执行器饱和是一种普遍的现象,它会严重的限制系统的动态性能而且引起系统的不稳定.为了降低饱和的影响,本文针对一类具有Brunovsky标准型以及输入饱和的不确定非线性系统提出了一种基于多层神经网络(MNNs)的自适应控制方法.该控制器由跟踪控制器和饱和补偿器组成.饱和补偿器是根据MNNs设计的,同时考虑了网络重构误差和外部干扰.自适应律是由Lyapunov函数得到的.通过Lyapunov定理证明了闭环系统是一致最终有界的.仿真结果验证了该方法的有效性.更多还原

【Abstract】 In actual systems,actuator saturation is a common phenomenon,which often severely restricts system dynamic performance and gives rise to instability.In order to reduce the effects of saturation,this paper presents an adaptive control method based on multilayer neural networks(MNNs) for a class of uncertain nonlinear systems with Brunovsky canonical form and input saturation.This controller is composed of a tracking controller and a saturation compensator. The saturation compensator is designed by MNNs which considers the network reconstruction error and the system’s external disturbance.The adaptation laws are derived in the sense of Lyapunov function.The closed-loop system is uniformly ultimately bounded,which is proved by Lyapunov theory.The simulation example is given to illustrate the effectiveness of this method.更多还原