Multi-agent systems consensus and containment analysis under nonconvex control constraints: a terminal iterative learning strategy

This paper aims to address the consensus and containment control problems of multi-agent systems by designing iterative learning controllers in the presence of nonconvex constraints. A difficulty in solving such problems is the lack of an efficient tool for dealing with the nonconvex constraints in distributed control. To tackle the issue, this paper proposes a novel class of iterative learning strategies in line with the nonconvex constraint operator approach. Then, we apply them in consensus analysis of multi-agent systems under the constraints. Furthermore, the containment control problem of multi-agent systems is investigated by designing terminal iterative learning strategies in the presence of nonconvex constraints where the coordination objective is to make the states of agents at the terminal time converge into the convex hull generated by those of the leaders. Under the framework of iterative learning control, combined with the contractive mapping technique and Lyapunov stability theory, some sufficient conditions are established for consensus and containment problems with terminal iterative learning strategies under nonconvex constraints. Finally, two numerical simulations are presented for illustration.