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For data-driven iterative learning control (ILC) methods, both the modelestimation and controller design problems are converted to parameter estimationproblems for some chosen model structures. It is well-known that if the modelorder is not chosen carefully, models with either large variance or large biaswould be resulted, which is one of the obstacles to further improve themodeling and tracking performances of data-driven ILC in practice. An emergingtrend in the system identification community to deal with this issue is usingregularization instead of the statistical tests, e.g., AIC, BIC, and one of therepresentatives is the so-called kernel-based regularization method (KRM). Inthis paper, we integrate KRM into data-driven ILC to handle a class ofrepetitive linear time-varying systems, and moreover, we show that the proposedmethod has ultimately bounded tracking error in the iteration domain. Thenumerical simulation results show that in contrast with the least squaresmethod and some existing data-driven ILC methods, the proposed one can givefaster convergence speed, better accuracy and robustness in terms of thetracking performance.

Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems