Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach

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• 作者：Yumei Sun^a ; ^b ; Bing Chen ; ^a ; ^{chenbing1958@126.com" class="auth_mail" title="E-mail the corresponding author} ; Chong Lin^a ; Honghong Wang^a ; Shaowei Zhou^a ; ^b
• 关键词：Adaptive neural control ; Stochastic nonlinear systems ; Backstepping ; State feedback
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 November 2016
• 年：2016
• 卷：369
• 期：Complete
• 页码：748-764
• 全文大小：1557 K

文摘

This paper addresses adaptive neural control for a class of stochastic nonlinear systems which are not in strict-feedback form. Based on the structural characteristics of radial basis function (RBF) neural networks (NNs), a backstepping design approach is extended from stochastic strict-feedback systems to a class of more general stochastic nonlinear systems. In the control design procedure, RBF NNs are used to approximate unknown nonlinear functions and the backstepping technique is utilized to construct the desired controller. The proposed adaptive neural controller guarantees that all the closed-loop signals are bounded and the tracking error converges to a sufficiently small neighborhood of the origin. Two simulation examples are used to illustrate the effectiveness of the proposed approach.