文章基本信息

标题：Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning
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作者：Arash Bahari Kordabad ; Sebastien Gros
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：308-313
DOI：10.1016/j.ifacol.2021.08.562
语种：English
出版社：Elsevier
摘要：AbstractIn the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop stability relates to the existence of a storage function satisfying a dissipation inequality. Finding the storage function is in general– for nonlinear dynamics and cost– challenging, and has attracted attentions recently. Q-Learning is a well-known Reinforcement Learning (RL) techniques that attempts to capture action-value functions based on the state-input transitions and stage cost of the system. In this paper, we present the use of the Q-Learning approach to obtain the storage function and verify the dissipativity for discrete-time systems subject to state-input constraints. We show that undiscounted Q-learning is able to capture the storage function for dissipative problems when the parameterization is rich enough. The efficiency of the proposed method will be illustrated in the different case studies.
关键词：KeywordsEconomic Nonlinear Model Predictive ControlReinforcement LearningQ-learningDissipativityStorage Function