  For data-driven iterative learning control (ILC) methods, both the model
estimation and controller design problems are converted to parameter estimation
problems for some chosen model structures. It is well-known that if the model
order is not chosen carefully, models with either large variance or large bias
would be resulted, which is one of the obstacles to further improve the
modeling and tracking performances of data-driven ILC in practice. An emerging
trend in the system identification community to deal with this issue is using
regularization instead of the statistical tests, e.g., AIC, BIC, and one of the
representatives is the so-called kernel-based regularization method (KRM). In
this paper, we integrate KRM into data-driven ILC to handle a class of
repetitive linear time-varying systems, and moreover, we show that the proposed
method has ultimately bounded tracking error in the iteration domain. The
numerical simulation results show that in contrast with the least squares
method and some existing data-driven ILC methods, the proposed one can give
faster convergence speed, better accuracy and robustness in terms of the
tracking performance.
