Leader–follower consensus control for a class of nonlinear multi-agent systems using dynamical neural networks

Abstract

This paper addresses the formation consensus tracking control problem of uncertain dynamical multi-agent system with nonlinear dynamics based on a leader–follower configuration based on a undirected communication topology. The control design is based on a novel combination of consensus protocols and dynamical neural networks, known as differential neural networks, to approximate the modeling uncertainties and external disturbances of the follower agents. The proposed controller-identifier structure forces the state of each agent to maintain a formation with respect to the leader ensuring limited residual errors. The stability proof supports the control design and guarantees the multi-agent system states to be ultimately bounded. In order to show the effectiveness of the proposed tracking controller and the superior performance against the static RBFNNs approach, some simulation results are presented considering a multi-robotic system with five agents following a helical three-dimensional reference signal of a virtual leader.