基于平均一致协议的分布式自适应多智能体聚集控制

Distributed Adaptive Multi-agent Rendezvous Control Based on Average Consensus Protocol

分布式聚集控制问题是多智能体协同控制中的一个重要问题。由于智能体的可移动性和感知能力有限,传统的分布式聚集算法难以保证连通性,从而聚集成多个簇群。此外,去中心化的大规模聚集控制给获取全局聚集点带来了巨大的挑战。针对连通性保护问题,基于平均一致协议与约束集,提出了一个带有连通性约束的多智能体聚集协议(Multi-Agent Rendezvous Protocol with Connectivity constraints,MARP-CC)。然后针对聚集点无法预测的问题,提出了位置合成(Location Synthesis Strategy,LSS)和位置重定向(Location Redirection Strategy,LRS)两种控制策略。智能体根据当前连通情况,自适应选择最优的控制策略进行迭代。结合这两种控制策略,提出了带连通性约束的分布式自适应多智能体聚集算法(Distributed Adaptive Multi-Agent Rendezvous algorithm with Connectivity Constraints,DAMAR-CC)。对算法的收敛性和连通性进行了分析,并通过大量的仿真说明了DAMAR-CC可以引导智能体稳定地聚集在初始拓扑的几何中心。

Distributed rendezvous control is an important issue in multi-agent collaborative control.Due to the limited mobility and perception capabilities of agents,traditional distributed rendezvous algorithms are difficult to ensure connectivity,thereby aggregating multiple clusters.In addition,decentralized large-scale rendezvous control poses a huge challenge to obtaining global rendezvous points.For the connectivity protection problem,based on the average consensus protocol and constraint set,a multi-agent rendezvous protocol with connectivity constraints(MARP-CC)is proposed.Then,for the rendezvous point unpredictability problem,location synthesis(LSS)and location redirection(LRS)control strategies are proposed.The agent adaptively selects the optimal control strategy for iteration based on the current connectivity situation.Finally,combining these two control strategies,a distributed adaptive multi-agent rendezvous algorithm with connectivity constraints(DAMAR-CC)is proposed.The conver-gence and connectivity analysis of the algorithm are given,and a large number of simulations show that DAMAR-CC can make agents stably rendezvous at the geometric center of the initial topology.