Performance Optimization For Consensus Of Multi-Agent Systems

In recent years,the performance optimization for consensus of multi-agent system has attracted extensive attention of many researchers in the field of optimization control.Among them,the convergence rate of consensus and the energy consumption of multi-agent system is the main focus of performance optimization.The performance optimization problem of multi-agent system is that the given performance evaluation index which is optimized by controller optimization or topology optimization with some kind of controller.The objective of optimization is to make the system accomplish the assigned task with the best performance.Therefore,this paper will use topology optimization,controller optimization and the combination of both to enhance the consensus performance of multi-agent system.Firstly,the convergence speed can be improved by the optimization of topology.Topological graph mainly consists of two parts:edge and edge weight.Therefore,we can proceed from these two aspects to optimize the system performance.In the condition of given topology,the convergence speed is improved by optimizing the edge weight of topological graph,but this method has limitation.In this paper,a new method is adopted to improve the convergence rate of the consensus by changing the edges reasonably.In order to achieve this goal,we introduce a community partition algorithm in complex networks.It also generally analyzes that the convergence rate of consensus can be improved by the combination of community partition algorithm and consensus protocol.Simulation study verifies the feasibility and effectiveness of this method.Secondly,the convergence speed and the energy consumption of system can be optimized by the controller optimization.A distributed optimum controller with constraints is obtained by combining the LQR optimal control with the Riccati equation for general linear systems.Thus,the energy consumption can be reduced to the maximum and the convergence speed can be improved.Through the optimization of the controller,a parameter equation that determines the convergence rate is obtained,and the desired convergence rate can be obtained by adjusting the parameters in the parameter equation.Finally,the optimal controller is obtained by combining the LQR optimal control with the Riccati equation.However,this optimization process does not consider the improvement effect of topology optimization.So this chapter will further consider the topology optimization design on the basis of the optimal controller,so as to maximize the performance of the system.