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.

翻译：本文利用迭代学习控制（ILC）实现了具有非重复时变参数的时间-varying系统（TVSs）的重复跟踪任务，这类系统也称为非重复TVSs。针对非重复TVSs，提出了一种基于机器学习（ML）的名义模型更新机制，该机制利用线性回归技术，仅使用当前试验信息在每个ILC试验中更新名义模型，以提升ILC性能。鉴于ML机制迫使模型不确定性保持在ILC鲁棒容差范围内，提出了一种处理非重复TVSs的ILC更新律。同时给出了如何调整ML和ILC算法参数以实现期望的整体性能的方法。通过仿真验证了所提方法的鲁棒性和可靠性。与当前最先进技术的比较表明，该方法在控制精度方面具有优越的控制性能。本文的主要贡献在于：将ILC应用从时不变系统扩展到非重复TVSs，采用ML回归技术估计两个ILC试验之间的非重复时变参数，并提出详细的参数调节机制以实现期望性能。