Bipartite Consensus Research Of Multi-agent Systems Based On Disturbances

In recent years,as a new type of distributed computing technology,multi-agent systems have been widely used in social,military and industrial fields,attracting the research interest of scientific researchers.The bipartite consensus of multi-agent systems with external disturbances is investigated in this paper,and the practical factors such as the different system models,the high convergence rate and good performance are considered.The main content is as follows.For the actual demand of convergence time in the bipartite consensus control of multi-agent systems,a fixed-time control algorithm is designed via the related knowledge of Lyapunov stability theory and algebraic graph theory,and the fixed-time bipartite consensus problem is studied under there exists disturbances or not,and a sufficient condition for achieving the distributed fixed-time bipartite consensus is derived.It is shown that under the presented algorithm,all agents can achieve bipartite consensus in fixed time and the settling time does not depend on the initial state of the agent.For the second-order leader-following multi-agent system with external disturbance,a disturbances observer is proposed to effectively estimate agent's disturbance.Using the Lipschitz condition,linear matrix inequality method and pinning control,assume that only part of agents can receive the leader's information,the bipartite tracking consensus of nonlinear multi-agent system is achieved.For the heterogeneous multi-agent system with disturbance over signed graph,the output bipartite consensus is investigated.Two cases that agent's information is accessible or not are considered.If the information is accessible,using the output regulation theory,the state feedback bipartite consensus algorithm is proposed.If the information is not accessible,an output feedback bipartite consensus algorithm is developed by state observer,and sufficient conditions for reaching the output feedback bipartite consensus are derived for the multi-agent systems and exogenous system.