文章基本信息

标题：Learning Robustness with Bounded Failure: An Iterative MPC Approach
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作者：Monimoy Bujarbaruah ; Akhil Shetty ; Kameshwar Poolla 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：7085-7090
DOI：10.1016/j.ifacol.2020.12.462
语种：English
出版社：Elsevier
摘要：AbstractWe propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task. The system is subject to an additive disturbance, and the goal is to learn to satisfy state and input constraints robustly. Using disturbance measurements after each iteration, we construct Confidence Support sets, which contain the true support of the disturbance distribution with a given probability. As more data is collected, the Confidence Supports converge to the true support of the disturbance. This enables design of an MPC controller that avoids conservative estimate of the disturbance support, while simultaneously bounding the probability of constraint violation. The efficacy of the proposed approach is then demonstrated with a detailed numerical example.
关键词：KeywordsPredictive Control for Linear SystemsIterative Predictive ControlRobust Convex OptimizationConfidence IntervalsBootstrap