Adaptive neural leader-following consensus control for a class of nonlinear multi-agent systems

In this paper, an adaptive neural consensus tracking algorithm for a class of nonlinear multi-agent systems is studied. The Radial Basis Function Neural Networks (RBFNNs) are utilized to model the unknown nonlinear function of multi-agent system dynamic. Based on Lyapunov analysis method, it is proven that the nonlinear multi-agent system is stable and the consensus tracking errors can converge to a small neighborhood of origin by applied the proposed control method. The effectiveness of the developed scheme is illustrated by a simulation example.