This paper studies edge-based event-triggered strategies for the average consensus problem in multi-agent systems. To this end, a distributed event-triggered algorithm is presented based on edge information rather than neighbor information, where positive minimum inter-event times are guaranteed between all communication links in the network. Moreover, the triggering mechanism is constructed in a fully distributed way which uses only relative state differences based on local neighborhood information without any global graph topology information. This treatment greatly reduces the communication frequency compared with continuous-time average consensus algorithms. We show that the proposed algorithm makes all agents converge to the average of their initial states asymptotically, and the positive minimum inter-event time for each link is controllable by two corresponding agents through their triggering parameters.

本文研究了多智能体系统中平均共识问题的基于边缘的事件触发策略。为此，提出了一种基于边缘信息而不是邻居信息的分布式事件触发算法，其中保证网络中所有通信链路之间的最小事件间时间为正。此外，触发机制以完全分布式的方式构建，仅使用基于局部邻域信息的相对状态差异，没有任何全局图拓扑信息。与连续时间平均共识算法相比，这种处理大大降低了通信频率。我们表明，所提出的算法使所有代理渐近地收敛到其初始状态的平均值，