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Iterative Learning Control for Discrete Singular Systems with Randomly Varying Trial Lengths

This paper researches iterative learning control for a class of singular systems with randomly iteration varying lengths. Based on an equivalence decomposition of discrete singular systems, a new learning algorithm with a stochastic variable and moving average operator is used to cope with the state tracking problem under non-uniform trial lengths circumstance. The stochastic variable is included both in tracking error and control input. Furthermore, the convergence condition of the proposed learning scheme is put forward and strictly proved. In the end, a numerical example is presented to demonstrate the effectiveness of the theoretical results.