Stochastic bipartite consensus with measurement noises and antagonistic information

Abstract

This paper is dedicated to the stochastic bipartite consensus issue of discrete-time multi-agent systems subject to additive/multiplicative noise over antagonistic network, where a stochastic approximation time-varying gain is utilized for noise attenuation. The antagonistic information is characterized by a signed graph. We first show that the semi-decomposition approach, combining with Martingale convergence theorem, suffices to assure the bipartite consensus of the agents that are disturbed by additive noise. For multiplicative noise, we turn to the tool from Lyapunov-based technique to guarantee the boundedness of agents’ states. Based on it, the bipartite consensus with multiplicative noise can be achieved. It is found that the constant stochastic approximation control gain is inapplicable for the bipartite consensus with multiplicative noise. Moreover, the convergence rate of stochastic MASs with communication noise and antagonistic exchange is explicitly characterized, which has a close relationship with the stochastic approximation gain. Finally, we verify the obtained theoretical results via a numerical example.