adaptive leaderfollowing and leaderless consensus of a class of nonlinear systems using neural networks

This paper deals with leaderfollowing and leaderless consensus problems of highorder multiinput/multioutput (mimo) multiagent 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 closedloop 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.