Distributed adaptive cooperative control for uncertain nonlinear multi-agent systems with hysteretic quantized input

Abstract

This paper studies the consensus control for nonlinear multi-agent systems on weighted directed communication graph. The dynamics of each follower agent contain hysteretic quantized input, unknown nonlinearities and unknown time-varying but bounded external disturbances. The adaptive distributed controller for each follower agent is constructed using information only from itself and its neighbors. By employing neural networks to handle uncertain nonlinearities and external disturbances, a consensus tracking control strategy is constructed based on backstepping techniques. By employing the proposed scheme, it is guaranteed that all system signals are bounded and the output of every follower well tracks the output of leader to an adjustable accuracy. Furthermore, there are only 2N (N is the number of the followers) online estimators need to be constructed in the proposed control strategy. The hysteretic quantizer for each follower agent can be different in the range of dead-zone and the coarseness of quantized level. Finally, the simulation results illustrate the effectiveness of the presented approach.