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.