Improvement of LMI controllers of Takagi-Sugeno models via Q-learning <ce:cross-ref id='cecref0001' refid='fn1'> <ce:sup loc='post'>*</ce:sup> </ce:cross-ref>

Henry Díaz; Leopoldo Armesto; Antonio Sala

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Improvement of LMI controllers of Takagi-Sugeno models via Q-learning *

机译：通过Q学习 *

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This 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.

机译：本文提出了一种初步的尝试，以将保证成本的LMI和强化学习设置（从数据中学习最佳控制器）之间的保守（形状无关）结果联系起来。从这个意义上讲，所提出的方法使用基于LMI解决方案的初始化，并使用Takagi-Sugeno模型中隶属函数的多项式来提出Q函数的近似值。最终的控制器取决于形状，即使用隶属函数和数据的知识来明显改善LMI解决方案。

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来源
《IFAC PapersOnLine》 |2016年第5期|共6页
作者
Henry Díaz; Leopoldo Armesto; Antonio Sala;
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中图分类自动化技术、计算机技术;
关键词
Reinforcement learningadaptive dynamic programmingQ-learningTakagi-SugenoLMI;

机译：强化学习自适应动态规划Q学习Takagi-SugenoLMI;

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Improvement of LMI controllers of Takagi-Sugeno models via Q-learning *

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