Neural Network-Based Adaptive Tracking Control for a Class of Uncertain Stochastic Nonlinear Pure-Feedback Systems

摘要：

　　In this paper,based on the well- known back-stepping method,a novel adaptive neural network (NN) control scheme is introduced to achieve a desired tracking performance for a class of uncertain stochastic nonlinear pure-feedback systems.The neural networks are utilized to approximate unknown functions in analysis procedure.Based on the key assumption,the adaptive NN controller only needs to adjust less adaptive parameters,therefore,it is clear that the proposed approach can reduce on-line computation burden.It is proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB) and the tracking error can converge to a small neighborhood of zero by choosing the appropriate design parameters.A simulation example is used to verify the effectiveness of the proposed approach.

关键词： Adaptive control Neural networks Stochastic nonlinear systems Back-stepping design scheme

作者: WANG Rui YU Fu-sheng WANG Jia-yin

作者单位: School of Mathematical Sciences, Laboratory of Complex System and Intelligent Control Beijing Normal University, Beijing, 100875, P.R. China

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 495-500

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）