文章基本信息

标题：Nearly Optimal Tunable MPC Strategies on Embedded Platforms
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作者：Karol Kiš ; Peter Bakaráč ; Martin Klaučo 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：16
页码：326-331
DOI：10.1016/j.ifacol.2022.09.045
语种：English
出版社：Elsevier
摘要：AbstractWe present an embeddable optimization-free application of a near-optimal MPC implementation with continuous tuning capabilities. We propose a strategy combining the advantages of explicit model predictive control with tunable properties that is implementable on embedded platforms with limited memory and computational resources. We consider a neural network (NN) learning procedure to mimic the control actions of an MPC strategy. While acknowledging limited guarantees on the constraints satisfaction with just the NN-based controller, we introduce an optimization-based corrector of the mimicked control action. Such a corrector then steers the control authority of the mimicked controller such that constraints on manipulated and process variables are enforced. To demonstrate the efficacy of the proposed control strategy, a case study implemented on an embedded platform is shown.
关键词：KeywordsData-based optimal controlReal-Time Control ProblemsOptimal Control