Machine learning based iterative learning control for non-repetitive time-varying systems
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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2023-05-10
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en
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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Abstract
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.Description
Funding Information: Entrepreneurship and Innovation Plan of Jiangsu Province, Grant/Award Number: JSSCBS20210641; National Natural Science Foundation of China, Grant/Award Number: 62103293; Natural Science Foundation of Jiangsu Province, Grant/Award Number: BK20210709; Suzhou Municipal Science and Technology Bureau, SYG202138 Funding information Funding Information: This research article was invested by National Natural Science Foundation of China under Grant 62103293, Natural Science Foundation of Jiangsu Province under Grant BK20210709, Entrepreneurship and Innovation Plan of Jiangsu Province under Grant JSSCBS20210641, and Suzhou Municipal Science and Technology Bureau under Grant SYG202138. Publisher Copyright: © 2022 John Wiley & Sons Ltd.
Keywords
iterative learning control, linear regression, machine learning, parameter estimation, time-varying systems
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Chen, Y, Jiang, W & Charalambous, T 2023, ' Machine learning based iterative learning control for non-repetitive time-varying systems ', International Journal of Robust and Nonlinear Control, vol. 33, no. 7, pp. 4098-4116 . https://doi.org/10.1002/rnc.6272