A Robust Sliding Mode Control With RBFNN Compensation For Uncertain Networked Control System

Liman Yang, Yunhua Li, Li Zuo
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Abstract

For the uncertain NCS with stochastic network delay less than one period, a sort of RBFNN-DSMC algorithm combining discrete sliding mode control and RBF neural network is presented. In view of the coupling influence of time-variable delay and plant model error as well as exterior disturbance, RBFNN is used to approach the equivalent disturbance online and output assistant control quantity so as to restrain uncertainty with the discrete sliding mode controller with delay compensation together. Simulation study indicates that the above-mentioned algorithm has good control performance and robustness for the uncertain NCS.
基于RBFNN补偿的不确定网络控制系统鲁棒滑模控制
针对随机网络时延小于一个周期的不确定网络控制,提出了一种将离散滑模控制与RBF神经网络相结合的RBFNN-DSMC算法。针对时变时滞和对象模型误差以及外部扰动的耦合影响,采用RBFNN在线逼近等效扰动和输出辅助控制量,与具有时滞补偿的离散滑模控制器共同抑制不确定性。仿真研究表明,该算法对不确定网络控制系统具有良好的控制性能和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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