Adaptive bipartite consensus of multi-agent systems with high-order dynamics and antagonistic interactions

Abstract

In this paper, a bipartite consensus problem is formulated for a multi-agent system with antagonistic interactions and high-order dynamics suffering from unknown disturbances. With the help of linearly parameterized models describing the unknown disturbances, decentralized adaptive estimators are designed to estimate the unknown parameters in the disturbances. Then, a distributed consensus control is developed for each agent to guarantee the bipartite consensus for the group. Furthermore, the convergence of the parameter estimate errors and the bipartite consensus is analyzed by using a Lyapunov function method. Finally, some simulation results are presented to demonstrate the form of the bipartite consensus on the coopetition network.