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Research on vehicle rectifier control strategy based on reinforcement learning
ID：41 View Protection：ATTENDEE Updated Time：2023-11-20 13:45:35 Hits：981 Oral Presentation

Start Time：2023-12-09 10:30（Asia/Shanghai）

Duration：15min

Session：S2 Power electronic technology and application » S2Power electronic technology and application

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Abstract
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.
 
Keywords
vehicle rectifier； reinforcement learning； dq current decoupling control ；optimal control .
Speaker
Mingwei Tang
student Southwest Jiaotong University

Submission Author
Mingwei Tang Southwest Jiaotong University
Zhigang Liu School of Electrical Engineering； Southwest Jiaotong University
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Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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