基于社区分解的多智能体递阶一致性仿真研究

Simulation Study on Hierarchical Consensus of Multi-Agent Systems Based on Community Decomposition

在多智能体系统的收敛速度一致性问题的研究中,传统的一致性算法一般都是在单层拓扑结构上讨论的。在设计边权数或者调整连接边实现提高系统的一致性收敛速度的过程中,针对类似的方法在很多情况下是受限制的。为了解决上述问题,提出一种社区分解的递阶一致性算法,采用社区分解方法对多智能体拓扑结构优化分解,进而将多智能体系统单层一致性问题转化为多层一致性问题,在维持原有拓扑结构约束的情况下提高系统的一致性收敛速度。针对一阶线性系统,通过与标准一致性算法的仿真比较,验证了改进算法的有效性。

The convergence speed of multi-agent system is a focused issue in the consensus problem.A hierarchical consensus algorithm based on the community decomposition was proposed in the paper,the community decomposition algorithm was applied to the optimum decomposition problem of the multi-agent topology.Then by converting the single-layer consensus problem of the multi-agent system to multi-layers consensus problem,the convergence speed was improved on the premise of maintaining the original topology constraints.For the first-order linear system,the effectiveness of the algorithm was demonstrated by simulations compared with the standard model.