Robust Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multi-agent Systems

This paper presents a robust adaptive neural consensus tracking control design for a class of nonlinear multi-agent systems with unknown nonlinear dynamic function. A Radial Basis Function Neural Network (RBFNN) is used as a universal approximation to reduce the model uncertainties coming from uncertain nonlinearities and to improve tracking performance. One main advantage of the proposed control approach is that the robustness of the nonlinear multi-agent systems is improved. Finally, it is prove the consensus tracking error convergence to a small neighborhood by Lyapnuov stability theory. A simulation is used to demonstrate the effectiveness of the developed scheme.