T-S fuzzy model-based adaptive repetitive learning consensus control of high-order multiagent systems with imprecise communication topology structure

摘要

This paper addresses the consensus problem for high-order nonlinear multiagent systems with imprecise communication topology structure (ICTS) and unknown periodic time-varying parameters. Takagi-Sugeno fuzzy models are used to portray the ICTS. By using the reparameterization technique, the repetitive learning control protocol is presented to guarantee that all the followers can track the leader asymptotically under the condition that the ICTS is fuzzy union connected. The information of the leader is known to a small portion of following agents; an auxiliary control term is presented for each follower agent to handle leader's dynamics. The consensus performance is analyzed via Lyapunov stability theory. Furthermore, the proposed protocol is further promoted to solve the formation control problem. Finally, the validity of the proposed methods are verified by two simulation examples.