NNs-based event-triggered consensus control of nonlinear multi-agent systems with uncertain dynamics

In this paper, we consider the consensus problem for a class of uncertain nonlinear systems under an undirected communication topology via event-triggered approaches. The centralized and distributed event-triggered control schemes are presented utilizing neural networks (NNs) and event-driven mechanisms, where the advantages of the proposed control laws lie that they remove the requirement for exact priori knowledge about parameters of individual agents by taking advantage of NNs approximators and they save computing and communication resources since control tasks only execute at certain instants with respect to predefined threshold functions. It is proven that all signals in the closed-loop system are bounded and Zeno behavior is excluded.