Adaptive average bipartite consensus control of high-order multi-agent systems on coopetition networks

Abstract

In this paper, an average bipartite consensus control problem is investigated for a high-order multi-agent system with non-identical unknown nonlinear dynamics on a coopetition network. Firstly, the average bipartite consensus problem is formulated for a high-order multi-agent system. Then, linearly parameterized models are used to describe the unknown dynamics. Simultaneously, adaptive estimation laws are designed for the unknown dynamics and time-varying coupling weights as well. Furthermore, a bipartite consensus control is proposed for each agent with the help of the adaptive laws to guarantee the average bipartite consensus. The convergence of the close-loop multi-agent system under the proposed consensus control is analyzed. Finally, simulation results are provided to validate the theoretical results.