Adaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks

This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the controllers simplify their implementation and reduce computational cost. Unknown nonlinearities are estimated by a radial basis function neural network (RBFNN). The ultimate boundness of the closed-loop system is guaranteed through Lyapunov stability analysis by introducing a suitably driven adaptive rule. Finally, the simulation results verify performance of the proposed control method.