Observer-based adaptive neural control for a class of nonlinear pure-feedback systems

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• 作者：Honghong WangAuthor Vitae ; Bing Chen ; ^{chenbing1958@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chong LinAuthor Vitae ; Yumei SunAuthor Vitae
• 关键词：Adaptive control ; Observer ; Neural networks ; Backstepping ; Nonlinear pure-feedback systems
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：171
• 期：Complete
• 页码：1517-1523
• 全文大小：686 K

文摘

This paper focuses on the issue of adaptive neural output-feedback control for a class of nonlinear pure-feedback systems. A state observer is first constructed to estimate the unmeasurable state variables. Radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and backstepping adaptive technique is utilized to construct controller. The proposed control scheme guarantees semi-globally uniformly ultimate boundedness (SGUUB) of all the signals in the closed-loop system. At last, a simulation example is used to show the effectiveness of the proposed method.