Adaptive NN Consensus Control for a Class of Nonlinear Multi-agent Time-Delay Systems

This paper studies an adaptive neural consensus control for a class of nonlinear multi-agent time delay systems. The Radial Basis Function Neural Networks (RBFNN) are utilized to approximate the unknown nonlinear function of system dynamic. Based on Lyapunov analysis method, it is proven that the nonlinear multi-agent system is stable and the consensus errors converge to a small neighborhood of zero. In contrast to the existing results, the advantage of the developed scheme is that the influence of time delay on the nonlinear multi-agent systems is eliminated. The effectiveness of the developed scheme is illustrated by a simulation example.