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Data loss can occur independently and randomly at different times and in different iterations with varying probabilities, leading to successive data dropouts on both the time and iteration axes. The proposed enhanced model-free adaptive iterative learning control controller incorporates a data compensation mechanism to compensate for missing data, ensuring excellent control performance. This data-driven control strategy requires only input\/output data for controller design. The convergence and effectiveness of the proposed approach are verified through rigorous mathematical analysis and simulation outcomes.<\/jats:p>","DOI":"10.1177\/00368504241229560","type":"journal-article","created":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T21:16:45Z","timestamp":1710710205000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Feedforward\u2013feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system"],"prefix":"10.1177","volume":"107","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-9359","authenticated-orcid":false,"given":"Shida","family":"Liu","sequence":"first","affiliation":[{"name":"The North China University of Technology's School of Electrical and Control Engineering, Beijing, 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