A class of event-triggered coordination algorithms for multi-agent systems on weight-balanced digraphs
A class of event-triggered coordination algorithms for multi-agent systems on weight-balanced digraphs
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DOI：
10.23919/acc.2018.8431314
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Ping Xu;Cameron Nowzari;Z. Tian
中科院分区：
文献类型：
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作者：
Ping Xu;Cameron Nowzari;Z. Tian
本文重新讨论了权重平衡有向图上的多智能体平均一致性问题。为了减少代理之间的通信，许多最近的作品已经考虑事件触发的通信和控制作为一种方法，以减少通信，同时仍然确保整个网络收敛到所需的状态。一种常见的方法是设计事件，使得特定选择的李雅普诺夫函数单调递减;然而，根据所选择的李雅普诺夫函数，瞬态行为可能会非常不同。因此，我们反而有兴趣考虑一类李雅普诺夫函数，使每个李雅普诺夫函数产生不同的事件触发协调算法来解决多智能体平均共识问题。所提出的算法类都保证指数收敛的结果网络和排除芝诺行为。这使我们能够轻松地考虑不同算法的实现，这些算法都保证正确性，以满足不同的性能需求。模拟来说明我们的研究结果。
This paper revisits the multi-agent average consensus problem on weight-balanced directed graphs. In order to reduce communication among the agents, many recent works have considered event-triggered communication and control as a method to reduce communication while still ensuring that the entire network converges to the desired state. One common way to do this is to design events such that a specifically chosen Lyapunov function is monotonically decreasing; however, depending on the chosen Lyapunov function the transient behaviors can be very different. Consequently, we are instead interested in considering a class of Lyapunov functions such that each Lyapunov function produces a different event-triggered coordination algorithm to solve the multi-agent average consensus problem. The proposed class of algorithms all guarantee exponential convergence of the resulting network and exclusion of Zeno behavior. This allows us to easily consider the implementation of different algorithms that all guarantee correctness to be able to meet varying performance needs. Simulations are provided to illustrate our findings.