Machine learning based iterative learning control for non-repetitive time-varying systems

The repetitive tracking task for time-varying systems (TVSs) with non-repetitive time-varying parameters at each trial, which is also called non-repetitive TVSs, is realized in this article 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 using only 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 real experiments. Real data comparison with current state-of-the-art methods demonstrates its superior control performance in terms of controlling precision. This article 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.