Distributed adaptive consensus for nonlinear multi-agent systems under event-triggered communication using a polynomial approach

Abstract

This article investigates the adaptive consensus tracking control of nonlinear multi-agent systems (MASs) under event-triggered communication (ETC). The discontinuity of triggered signals poses challenges to the design of backstepping controllers to require continuous state information. To solve this problem, a univariate polynomial approach is proposed which replaces the discontinuous parts in the triggered signal with a smooth function. The approach does not rely on the continuous information to an agent about the state of its neighbors. Then, by backstepping recursion and using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear function, the distributed adaptive consensus controller is designed. It is shown that all signals are uniformly bounded and the synchronization errors converge to a neighborhood of origin. The simulation results corroborate the effectiveness of the approach.