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Iterative consensus control for a class of nonlinear multi-agent systems with randomly varying iteration lengths

In this paper, a distributed iterative learning control method was proposed for a class of nonlinear multi-agent systems consensus tracking, in which the systems trial lengths randomly varying. It is assumed that all agents have the same iterative learning length in one operation; however the operation lengths iteratively varying and a stochastic variable was introduced to describe non-uniform issue. With the control of proposed learning protocol, it is shown that the proposed algorithm guarantees the convergence of tracking error and repetitive multi-agent systems perform high precision tracking performance with randomly varying lengths. An illustrative example further verified the theoretical result.