文章基本信息

标题：Reinforcement Learning based Design of Linear Fixed Structure Controllers
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作者：Nathan P. Lawrence ; Gregory E. Stewart ; Philip D. Loewen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：230-235
DOI：10.1016/j.ifacol.2020.12.127
语种：English
出版社：Elsevier
摘要：AbstractReinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, such as neural networks, to update the controller parameters at each time-step of the underlying process. In this work, we present a simple finite-difference approach, based on random search, to tuning linear fixed-structure controllers. For clarity and simplicity, we focus on PID controllers. Our algorithm operates on the entire closed-loop step response of the system and iteratively improves the PID gains towards a desired closed-loop response. This allows for embedding stability requirements into the reward function without any modeling procedures.
关键词：Keywordsreinforcement learningprocess controlPID controlderivative-free optimization