LAUSR

This letter studies the consensus problem of general linear multi-agent systems (GLMASs) with limitations of network bandwidth, by developing a novel distributed dynamic event-triggered protocol. First, by adopting an estimation algorithm and introducing an inner self-learning term into triggering conditions, a new distributed dynamic event-triggered protocol (DDETP) is proposed to schedule communications among adjacent agents. Then, based on the local state information, a distributed dynamic event-triggered protocol is developed, which can drive the GLMASs to reach exponential consensus and exclude the possible Zeno phenomena. It is proven that the developed DDETP reduces the number of information transmission adaptively, which means that less network bandwidth is required, with comparison to the existing consensus protocols. Finally, the effectiveness of the proposed DDETP is verified through a numerical simulation.