Adaptive bipartite consensus control of general linear multi-agent systems using noisy measurements

This paper considers bipartite consensus problem for general linear systems with communication noises and antagonistic interactions. The interaction network associated with the cooperative-competitive multi-agent system is modeled by a signed graph (called coopetition network in sequel) and the agent dynamics is described by a general linear system. Instead of using the stochastic-approximation method available for previous works, adaptive laws are firstly designed for heterogenous time-varying control gains for agents. Furthermore, a fully distributed controller is proposed for multi-agent systems with measurement noises. At the same time, the bipartite consensus errors are proved to be exponentially bounded in mean square and the convergence is analyzed with a stochastic Lyapunov function method. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed adaptive control strategy.