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Research on vehicle rectifier control strategy based on reinforcement learning
编号：41 访问权限：仅限参会人 更新：2023-11-20 13:45:35 浏览：519次 口头报告

报告开始：2023年12月09日 10:30（Asia/Shanghai）

报告时间：15min

所在会场：[S2] Power electronic technology and application [S2] Power electronic technology and application

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摘要
The vehicle rectifier includes various linear, nonlinear and intelligent control strategies. Reinforcement learning compensates traditional control strategies, but these control strategies have various shortcomings. This paper proposes a replacement control strategy based on reinforcement learning, which can effectively solve the shortcomings of previous control strategies. Based on the traditional dq current decoupling control, the voltage loop is removed and all PI controllers are replaced. The reward function, state observation and action output of the dq axis are designed according to the performance index and effect. The double rectifier control system is designed, trained and verified. Finally, in order to increase the explainability of the control based on reinforcement learning, the optimal control theory is used to explain.
 
关键词
vehicle rectifier； reinforcement learning； dq current decoupling control ；optimal control .
报告人
Mingwei Tang
student Southwest Jiaotong University

稿件作者
Mingwei Tang Southwest Jiaotong University
Zhigang Liu School of Electrical Engineering； Southwest Jiaotong University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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