Consensus of Nonlinear Multiagent Systems With Uncertainties Using Reinforcement Learning Based Sliding Mode Control

Abstract

This paper investigates distributed control protocols design for uncertain nonlinear multi-agent systems with the goal of achieving the optimal consensus. The critical challenges encountered when designing the optimal distributed control protocols are mainly caused by the internal coupling of agents, uncertainty and nonlinear dynamics. Communication delay among agents makes overcoming these challenges even more difficult. To this end, a novel sliding mode control design method is developed based on the sliding mode control principle and the reinforcement learning technique. The remarkable highlights of the developed method in this paper include the design of distributed sliding mode controllers and the integrated framework of sliding mode control and reinforcement learning, which bring the outcome of successfully learning the composite distributed control protocols for multi-agent systems. Thus, all agents can successfully eliminate the negative impacts brought by system uncertainties and communication delay among agents, and finally follow the leader with a nearly optimal approach. The reachability of sliding mode surfaces and the optimal consensus are rigorously proven and analyzed. Finally, simulation results illustrate the effectiveness of the developed method.