Adaptive neural control of nonlinear MIMO systems with unknown time delays

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• 作者：Tieshan  ; Li^a ;   ; ^c ;   ; ^{tieshanli@126.com} ; Ronghui  ; Li^a ; Dan  ; Wang^b[Author vitae]
• 关键词：Time-delay systems ; Adaptive control ; Neural networks ; Lyapunov&ndash ; Krasovskii functions
• 刊名：Neurocomputing
• 出版年：2012
• 出版时间：15 February 2012
• 年：2012
• 卷：78
• 期：1
• 页码：83-88
• 全文大小：383 K

文摘

In this paper, a novel adaptive NN control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF NNs are used to tackle unknown nonlinear functions, then the adaptive NN tracking controller is constructed by combining Lyapunov¨CKrasovskii functionals and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, triple problems of ¡°explosion of complexity? ¡°curse of dimension?and ¡°controller singularity?are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.