文章基本信息

标题：Improvement of LMI controllers of Takagi-Sugeno models via Q-learning *
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作者：Henry Díaz ; Leopoldo Armesto ; Antonio Sala 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：5
页码：67-72
DOI：10.1016/j.ifacol.2016.07.091
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a preliminary attempt to bridge the conservative (shape-independent) results from guaranteed-cost LMIs and the reinforcement learning setups which learn optimal controllers from data. In this sense, the proposed approach uses an initialization based on the LMI solution and proposes an approximation of the Q-function using polynomials of the membership functions in Takagi-Sugeno models. The resulting controller is shape-dependent, that is, uses the knowledge of membership functions and data to clearly improve LMI solutions.
关键词：KeywordsReinforcement learningadaptive dynamic programmingQ-learningTakagi-SugenoLMI