T-S fuzzy model-based adaptive repetitive consensus control for multi-agent systems with imprecise communication topology structure

This paper studies the consensus problem of multi-agent systems (MAS) with imprecise communication topology structure (ICTS). T-S fuzzy model is used to express the ICTS. Through repeated learning techniques, this paper designs a distributed learning protocol that enables all agents reach consensus with periodic uncertainty parameters. The periodic uncertainty parameters are compensated based on a repetitive learning design method. With the information of leader agent is known to a small portion of following agents, an auxiliary control term is presented for each follower agent to handle leader's dynamic. Under the condition that the ICTS is fuzzy union connected, the learning control protocol proposed in this paper makes all the agents reach an agreement. In addition, the proposed consensus learning protocol is further promoted to solve the formation control problem. Sufficient conditions are given for the consensus and formation problems of the MAS by constructing a composite energy function, respectively. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control protocol.