文章基本信息

标题：Online learning robust MPC: an exploration-exploitation approach ⁎
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作者：J.M. Manzano ; J. Calliess ; D. Muñoz de la Peña 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：5292-5297
DOI：10.1016/j.ifacol.2020.12.1210
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a predictive controller whose model is based on input-output data of the nonlinear system to be controlled. It uses a Lipschitz interpolation technique in which new data may be included in the database in real time, so the controller improves the system model online. An exploration and exploitation policy is proposed, allowing the controller to robustly and cautiously steer the system to the best reachable reference, even if the model lacks data in such region. The conditions needed to ensure recursive feasibility in the presence of output and input constraints and in spite of the uncertainties are given. The results are illustrated in a simulated case study.
关键词：KeywordsLearning controlNonlinear controlTarget trackingRobust stabilityPredictive controlSampled-data systemsOutput feedback