文章基本信息

标题：Output-Lifted Learning Model Predictive Control
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作者：Siddharth H. Nair ; Ugo Rosolia ; Francesco Borrelli 等
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：365-370
DOI：10.1016/j.ifacol.2021.08.571
语种：English
出版社：Elsevier
摘要：AbstractWe propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can be reconstructed using lifted outputs. For the considered class of systems, we show how to use historical trajectory data collected during iterative tasks to construct a convex value function approximation along with a convex safe set in a lifted space of virtual outputs. These constructions are iteratively updated with historical data and used to synthesize predictive control policies. We show that the proposed strategy guarantees recursive constraint satisfaction, asymptotic stability, and non-decreasing closed-loop performance at each policy update. Finally, simulation results demonstrate the effectiveness of the proposed strategy on the kinematic unicycle.
关键词：KeywordsLearningPredictive ControlStabilityRecursive Feasibility