Interventional consensus for high-order multi-agent systems with unknown disturbances on coopetition networks

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• 作者：Yanzhi WuAuthor Vitae ; Jiangping Hu ; ^{hjp_lzu@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yuping ZhangAuthor Vitae ; Yong ZengAuthor Vitae
• 关键词：Multi-agent systems ; Interventional bipartite consensus ; Distributed adaptive estimator ; Signed graph theory ; Dynamical system theory
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 June 2016
• 年：2016
• 卷：194
• 期：Complete
• 页码：126-134
• 全文大小：1345 K

文摘

In this paper, an interventional consensus problem is formulated mathematically with signed graph theory and dynamical system theory. The interaction network associated with a multi-agent system is modeled by a signed graph (called coopetition network in sequel) and the dynamics of each agent is described by a high order differential equation with a nonlinear unknown time-varying disturbance. Then a distributed interaction law is designed for each agent to drive all agents belonging to two competitive subgroups to reach a bipartite consensus on a reference signal, which is generated by an exogenous system (called leader in sequel). Simultaneously, some neural network (NN) based adaptive estimators are proposed to estimate the nonlinear disturbances in the agent dynamics. The convergence of the bipartite consensus is analyzed by using a Lyapunov function method. Finally, some simulation results are presented to demonstrate the formation of the bipartite consensus.