Neuro-adaptive consensus tracking of multiagent systems with a high-dimensional leader and directed switching topologies

Abstract

This brief addresses the practical consensus tracking problem for multiagent systems (MASs) with a high-dimensional leader and uncertain dynamics over directed switching topologies based on the neuro-adaptive approach. First, an observer is embedded in each follower to estimate the leader’s state. And it is proven that the observer will track the leader asymptotically if the average dwell time (ADT) is sufficiently large. Furthermore, a discontinuous feedback controller plus a compensation controller is designed for each follower such that the error between the followers’ states and their associated observers’ states will converge into a bounded set. It is worth noting that the obtained residual error depends neither on the neural network (NN) approximation error nor on the external disturbances. Finally, a simulation is performed to validate the obtained theoretical result.