  The repetitive tracking task for time-varying systems (TVSs) with
non-repetitive time-varying parameters, which is also called non-repetitive
TVSs, is realized in this paper using iterative learning control (ILC). A
machine learning (ML) based nominal model update mechanism, which utilizes the
linear regression technique to update the nominal model at each ILC trial only
using the current trial information, is proposed for non-repetitive TVSs in
order to enhance the ILC performance. Given that the ML mechanism forces the
model uncertainties to remain within the ILC robust tolerance, an ILC update
law is proposed to deal with non-repetitive TVSs. How to tune parameters inside
ML and ILC algorithms to achieve the desired aggregate performance is also
provided. The robustness and reliability of the proposed method are verified by
simulations. Comparison with current state-of-the-art demonstrates its superior
control performance in terms of controlling precision. This paper broadens ILC
applications from time-invariant systems to non-repetitive TVSs, adopts ML
regression technique to estimate non-repetitive time-varying parameters between
two ILC trials and proposes a detailed parameter tuning mechanism to achieve
desired performance, which are the main contributions.
