Fully distributed adaptive optimization event-triggered/self-triggered synchronization for multi-agent systems

Abstract

This paper investigates the distributed adaptive optimization synchronization problem of multi-agent systems (MASs) with general linear dynamics on undirected graphs. The goal is to fulfill the synchronization among agents and synergistically optimize the team cost function formed by a family of local convex functions. The tracking servo signal is first generated by sampling the implicit state of the axillary system, and its sampling events are governed by the triggering mechanism I. Meanwhile, the disagreement vector is sampled if the triggering mechanism II is violated. An adaptive event-triggered scheme is then constructed by the gradient term whose input is the tracking servo signal and the relative sampling information among followers, which fulfills the synchronization as the desired one and minimizes the team cost function. It proves that Zeno behavior is excluded under the triggering mechanisms I and II, respectively. Moreover, a self-triggered strategy is leveraged that depends only on the partial derivative of the local cost function in the implicit state sampling and the relative sampling information of itself and its neighbors; thus, continuously monitoring the information of neighbors is avoided. It is noted that the proposed scheme incorporates adaptive event-triggered control, which makes it possible to implement the fully distributed control manner. The efficacy and advantage of the presented theoretical results are finally demonstrated using a non-trivial simulation example.