Adaptive Neural Control for a class of Stochastic Nonlinear Systems using Stochastic Small-gain Theorem

摘要：

　　In this paper,a novel adaptive neural control scheme is presented for a class of stochastic strict-feedback nonlinear systems with dead-zone model and unmodeled dynamics using stochastic small-gain theorem.Radial basis function neural networks(RBFNNs)are utilized to approximate the unknown continuous functions.Compared with the existing work,the controller is simpler and the restriction of dynamic disturbances is relaxed.The stability analysis is given to show that all the signals in the closed-loop system are ISpS in probability.The effectiveness of the proposed design is illustrated by simulation results.

关键词： Stochastic Systems Dead-zone Model Unmodeled Dynamics Adaptive Neural Control Stochastic Small-gain Theorem

作者: GAO Hua-ting ZHANG Tian-ping WANG Ran-ran YI Yang

作者单位: Department of Automation,College of Information Engineering,Yangzhou University,Yangzhou 225127,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5409-5414

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）