{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T16:06:03Z","timestamp":1777910763011,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"15","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transactions of the Institute of Measurement and Control"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p>In this paper, we propose two data-driven adaptive tuning (DDAT) approaches of iterative learning control (ILC) for nonlinear non-affine systems. First, a compact-form iterative dynamic linearization (CFIDL) method is introduced to transfer the original nonlinear system into a linear data model. Then, we design an objective function for the tuning of the learning gains of a PD-type ILC law. By optimizing the designed cost function subjected to the linear data model, a CFIDL-based DDAT method is proposed, where only the real I\/O data are used without requiring any mechanistic model information. Furthermore, the results are extended by introducing a partial-form iterative dynamic linearization (PFIDL) method for the purpose of utilizing more additional control information. Following the similar steps, a PFIDL-based DDAT method is developed for learning gain tuning of the PD-type ILC scheme. Both the proposed DDAT methods can help the PD-type ILC have a better robustness against to the uncertainties since they can use the real I\/O data to iteratively tune the learning gains. The convergence of the DDAT-based PD-type ILC methods has been proved rigorously. The effectiveness of the two proposed DDAT-based ILC methods are further verified through simulations.<\/jats:p>","DOI":"10.1177\/01423312221099381","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T05:12:23Z","timestamp":1654060343000},"page":"3016-3027","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Data-driven adaptive tuning of iterative learning control"],"prefix":"10.1177","volume":"44","author":[{"given":"Yingzhen","family":"Yu","sequence":"first","affiliation":[{"name":"School of Automation & Electronics Engineering, Qingdao University of Science & Technology, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-3199","authenticated-orcid":false,"given":"Na","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Automation & Electronics Engineering, Qingdao University of Science & 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