Two novel iterative learning control schemes for systems with randomly varying trial lengths

This paper proposes two novel improved iterative learning control (ILC) schemes for systems with randomly varying trial lengths. Different from the existing works on ILC with variable trial lengths that advocate to replace the missing control information by zero, the proposed learning algorithms are equipped with a searching mechanism to collect useful but avoid redundant past tracking information, which could expedite the learning speed. The searching mechanism is realized by the newly defined stochastic variables and an iteratively-moving-average operator. The convergence of the proposed learning schemes is strictly proved based on the contraction mapping methodology. Two illustrative examples are provided to show the superiorities of the proposed approaches.