Adaptive neural control for a class of stochastic non-strict-feedback nonlinear systems with time-delay

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• 作者：Yumei Sun^a ; ^bAuthor Vitae ; Bing Chen^a ; ^{chenbing1958@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chong Lin^aAuthor Vitae ; Honghong Wang^aAuthor Vitae
• 关键词：Neural adaptive control ; Stochastic nonlinear systems ; Non-strict-feedback ; Backstepping ; Time-delay ; Lyapunov&ndash ; Krasovskii functional
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 November 2016
• 年：2016
• 卷：214
• 期：Complete
• 页码：750-757
• 全文大小：921 K

文摘

This paper addresses adaptive neural control for a class of non-strict-feedback stochastic nonlinear systems with time delays. An important structural property of radial basis function (RBF) neural networks (NNs) is introduced to overcome the design difficulty from the non-strict-feedback structure. The Lyapunov–Krasovskii functional is used for control design and stability analysis. Further, a backstepping-based adaptive neural control strategy is proposed. The suggested adaptive neural controller guarantees that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed approach.