文章基本信息

标题：Reducing the computational effort of min-max model predictive control with regional feedback laws ⁎
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作者：Kai König ; Martin Mönnigmann
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：6
页码：58-63
DOI：10.1016/j.ifacol.2021.08.524
语种：English
出版社：Elsevier
摘要：AbstractRecently, a regional MPC approach has been proposed that exploits the piecewise affine structure of the optimal solution (without computing the entire explicit solution before). Here,regionalrefers to the idea of using the affine feedback law that is optimal in a vicinity of the current state of operation, and therefore provides the optimal input signal without requiring to solve a QP. In the present paper, we apply the idea of regional MPC to min-max MPC problems. We show that the new robust approach can significantly reduce the number of QPs to be solved within min-max MPC resulting in a reduced overall computational effort. Moreover, we compare the performance of the new approach to an existing robust regional MPC approach using a numerical example with varying horizon. Finally, we provide a rule for choosing a suitable robust regional MPC approach based on the horizon.
关键词：KeywordsRobust controlpredictive controlquadratic programmingmodel-based controlconstraints