Asymptotical Neuro-Adaptive Consensus of Multi-Agent Systems With a High Dimensional Leader and Directed Switching Topology

We study the asymptotical consensus problem for multi-agent systems (MASs) consisting of a high-dimensional leader and multiple followers with unknown nonlinear dynamics under directed switching topology by using a neural network (NN) adaptive control approach. First, we design an observer for each follower to reconstruct the states of the leader. Second, by using the idea of discontinuous control, we design a discontinuous consensus controller together with an NN adaptive law. Finally, by using the average dwell time (ADT) method and the Barbalat's lemma, we show that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the ADT is larger than a positive threshold. Moreover, we study the asymptotical neuroadaptive consensus problem for MASs with intermittent topology. Finally, we perform two simulation examples to validate the obtained theoretical results. In contrast to the existing works, the asymptotical neuroadaptive consensus problem for MASs is firstly solved under directed switching topology.

Automated summary

This paper investigates the asymptotical consensus problem for multi-agent systems (MASs) with unknown nonlinear dynamics under directed switching topology using a neural network (NN) adaptive control approach. It designs an observer for each follower to reconstruct the states of the leader, and proposes a discontinuous consensus controller and an NN adaptive law based on the idea of discontinuous control. The paper proves theoretically that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the average dwell time (ADT) is larger than a positive threshold.