Distributed Adaptive optimization Via Edge-event-based Triggering

This paper investigates the distributed optimization problem for multi-agent systems. An adaptive algorithm with edge-event-based triggering is proposed to minimize a differential global objective function. It is shown that the proposed algorithm can ensure that the consensus error is asymptotically stabilized and the Zeno behaviour can be avoided. Moreover, a sampled-data scheme driven by edge-events is proposed to relax the requirement of continuous communication between neighbouring agents without the need of global information.