Adaptive consensus tracking of non-square MIMO nonlinear systems with input saturation and input gain matrix under directed graph

Abstract

This paper deals with the leader-following consensus problems of non-square multi-input/multi-output multi-agent systems in the presence of control input saturation and uncertain external disturbances. The agents have unknown high-order nonlinear dynamics and communicate together under any directed graph containing a spanning tree. A distributed adaptive method is designed to solve the problem. Moreover, a constant full-rank matrix with an adaptive gain is used instead of approximation of the unknown gain matrix by neural networks. Therefore, the proposed controller’s structure simplifies its implementation. The unknown nonlinearities are estimated by a radial basis function neural network. The ultimate boundness of the closed-loop system is guaranteed through Lyapunov stability analysis by introducing suitably driven adaptive rules. Finally, the simulation results verify the performance of the proposed control method.