Research on vehicle rectifier control strategy based on reinforcement learning
ID：41
Submission ID：80 View Protection：ATTENDEE
Updated Time：2023-11-20 13:45:35 Hits：411
Oral Presentation
Start Time：2023-12-09 10:30 （Asia/Shanghai）
Duration：15min
Session：[S2] Power electronic technology and application » [S2] Power 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 .
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