周期事件触发的多智能体分布式凸优化

Periodic Event-triggered Multi-agent Distributed Convex Optimization

基于周期事件触发机制,研究了具有时变时滞的多智能体系统在强连通有向拓扑下的分布式凸优化问题,提出了一种分布式事件触发零梯度和算法。与时间触发的分布式优化算法相比,该算法可以降低网络系统中的通信负载,具有能耗低和通信成本低的优点。此外,还证明了智能体的状态渐近收敛到全局最优点。由于事件仅在周期时刻进行检验,那么相邻事件触发时刻的时间间隔的下界是采样周期h,可以直接排除Zeno行为。最后通过数值模拟说明了理论结果的有效性。

In this paper, the distributed convex optimization problem of multi-agent systems with time-varying delays in strongly connected topology is studied based on the event-triggered mechanism, and a distributed event-triggered zero-gradient-sum algorithm is proposed. Compared with the time-triggered distributed optimization algorithm, this algorithm can reduce the communication load in the networked systems, and has advantages of low energy consumption and low communication cost. It is also proved that the state of each agent asymptotically converges to the global optimum. Since the event is only checked at the periodic instants,the lower bound of the time interval between two contiguous event-triggered instants is the sampling period h and Zeno behavior can be directly excluded. Finally, numerical simulation is given to illustrate the effectiveness of the theoretical results.