Xie Guangqiang, Xu Haoran, Li Yang, Wang Chang-Dong, Zhong Biwei, Hu Xianbiao
IEEE Trans Neural Netw Learn Syst. 2024 Nov;35(11):15810-15824. doi: 10.1109/TNNLS.2023.3290015. Epub 2024 Oct 29.
Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.
当智能体数量大幅增加时，多智能体共识问题的最新进展凸显了网络拓扑结构的作用。现有研究工作假设收敛演化通常在对等架构上进行，其中智能体被平等对待并直接与感知到的一跳邻居进行通信，从而导致收敛速度较慢。在本文中，我们首先提取骨干网络拓扑结构，以在原始多智能体系统（MAS）上提供分层组织。其次，我们在周期性提取的切换骨干拓扑结构下引入基于约束集（CS）的几何收敛方法。最后，我们推导了一个名为分层切换骨干多智能体系统（HSBMAS）的完全分散框架，该框架旨在使智能体收敛到一个共同的稳定平衡点。当初始拓扑结构连通时，提供了该框架可证明的连通性和收敛性保证。在不同类型和不同密度拓扑结构上的大量仿真结果表明了所提框架的优越性。
