{"title":"Data informativity for tracking control of learning systems: Test and design conditions","authors":"","doi":"10.1016/j.automatica.2024.111885","DOIUrl":null,"url":null,"abstract":"<div><p>How to develop control design methods directly based on the input and output data, instead of utilizing the system model, becomes a practically important topic in the control community. This paper explores the data informativity for the data-based control design methods, with a special focus on accomplishing the tracking objective for iterative learning control (ILC) systems. With the data collected under a certain test framework for ILC systems, a necessary and sufficient condition on the data informativity is provided for trackability of the desired reference, which is a premise for the realization of the perfect tracking objective. Based on the informative data for trackability, three classes of data-based ILC updating laws are designed to reach the perfect tracking objective for a group of ILC systems compatible with the informative data. Moreover, the data informativity for <span><math><mi>δ</mi></math></span>-trackability of the desired reference is discussed with the focus on the more general <span><math><mi>δ</mi></math></span>-tracking objective, under which a data-based ILC updating law is presented by only resorting to the informative data for <span><math><mi>δ</mi></math></span>-trackability. In addition, for ILC systems with noises, the collected noisy data are leveraged to further exploit an ILC updating law to achieve the robust tracking objective. All the developed data-based ILC updating laws are applicable for any linear ILC system despite whether it is irregular or not, where the specific system model is not required.</p></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0005109824003790/pdfft?md5=1cf1f278bc9040a4af566f4a3cb5ef02&pid=1-s2.0-S0005109824003790-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109824003790","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
How to develop control design methods directly based on the input and output data, instead of utilizing the system model, becomes a practically important topic in the control community. This paper explores the data informativity for the data-based control design methods, with a special focus on accomplishing the tracking objective for iterative learning control (ILC) systems. With the data collected under a certain test framework for ILC systems, a necessary and sufficient condition on the data informativity is provided for trackability of the desired reference, which is a premise for the realization of the perfect tracking objective. Based on the informative data for trackability, three classes of data-based ILC updating laws are designed to reach the perfect tracking objective for a group of ILC systems compatible with the informative data. Moreover, the data informativity for -trackability of the desired reference is discussed with the focus on the more general -tracking objective, under which a data-based ILC updating law is presented by only resorting to the informative data for -trackability. In addition, for ILC systems with noises, the collected noisy data are leveraged to further exploit an ILC updating law to achieve the robust tracking objective. All the developed data-based ILC updating laws are applicable for any linear ILC system despite whether it is irregular or not, where the specific system model is not required.
期刊介绍：
Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field.
After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience.
Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.
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