Fuzzy adaptive iterative learning control with initial-state learning for coordination of leader-following multi-agent systems with imprecise communication topology structure

Abstract

This paper proposes a distributed adaptive iterative learning control protocol for consensus problem of linearly parameterized multi-agent systems with imprecise communication topology structure. T-S fuzzy models are presented to describe the imprecise communication topology structure. The AILC protocols are designed with distributed initial-state learning and it is not essential to fix the initial value at the start of each iteration. Without using any global information, the proposed protocol ensures that the follower agents track the leader exactly on [0, T] for the consensus problem under distributed initial-state learning condition. Sufficient conditions of perfectly consensus for multi-agent systems are obtained by appropriately constructing Lyapunov function. Finally, the simulation example is given to verify the efficacy of the theoretical analysis.