Distributed containment output-feedback control for a general class of stochastic nonlinear multi-agent systems

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• 作者：Milad Shahvali^a ; ^{milad.shahvali@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Javad Askari^bAuthor Vitae
• 关键词：Containment control ; Dynamic surface control ; Output-feedback design ; Command filter ; Minimal learning parameter ; General stochastic nonlinear multi-agent systems
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：29 February 2016
• 年：2016
• 卷：179
• 期：Complete
• 页码：202-210
• 全文大小：1055 K

文摘

This study considers a distributed containment output-feedback control approach for a general class of stochastic uncertain nonlinear multi-agent systems. At first, local linear state observers are designed to deal with the unmeasured states. Then, radial basis function (RBF) neural networks (NNs) and minimal learning parameter approach are employed to approximate unknown nonlinearities. On the basis of dynamic surface control (DSC), command filter technique, adaptive neural approximator and linear observers, a simplified systematic approach to design of the coordinated containment output-feedback controller for stochastic uncertain nonlinear multi-agent systems is offered. In the proposed distributed controller the problems of explosion of complexity and effect of DSC filter errors are eliminated, simultaneously. Via Lyapunov theory, it is shown that the proposed controller can guarantee that all the signals in the closed-loop network system are cooperatively semi-globally uniformly ultimately bounded (CSGUUB) in the sense of mean square; meanwhile all followers׳ outputs converge to the dynamic convex envelope spanned by the dynamic leaders. Finally, simulation results are shown to confirm efficiency of the proposed method.