Data-driven Control for the Consensus of a Class Multi-agent Systems With Unknown Dynamics

This study investigates the consensus problem for a class of nonlinear discrete-time multi-agent systems. The dynamics of agents are heterogeneous and unknown. A novel distributed data-driven protocol is proposed by using model free adaptive control method, which only uses the input/output data of neighbouring agents and desired signal. Under the proposed protocol, the sufficient conditions that guarantee the consensus errors of unknown multi-agent systems uniformly bounded are analyzed. Finally, the effectiveness of the proposed protocol is illustrated through simulations.