Robust cooperative learning control for directed networks with nonlinear dynamics

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• 作者：Deyuan Meng^a ; ^b ; ^{dymeng@buaa.edu.cn}Author Vitae ; Kevin L. Moore^c ; ^{kmoore@mines.edu}Author Vitae
• 关键词：Cooperative learning ; Nonlinear agents ; Relative formation ; Switching topologies ; Initial state shifts ; Disturbances
• 刊名：Automatica
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：75
• 期：Complete
• 页码：172-181
• 全文大小：717 K
• 卷排序：75

文摘

This paper studies a class of robust cooperative learning control problems for directed networks of agents (a) with nonidentical nonlinear dynamics that do not satisfy a global Lipschitz condition and (b) in the presence of switching topologies, initial state shifts and external disturbances. All uncertainties are not only time-varying but also iteration-varying. It is shown that the relative formation of nonlinear agents achieved via cooperative learning can be guaranteed to converge to the desired formation exponentially fast as the number of iterations increases. A necessary and sufficient condition for exponential convergence of the cooperative learning process is that at each time step, the network topology graph of nonlinear agents can be rendered quasi-strongly connected through switching along the iteration axis. Simulation tests illustrate the effectiveness of our proposed cooperative learning results in refining arbitrary high precision relative formation of nonlinear agents.