文章基本信息

标题：Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control
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作者：Jiawen Li ; Yaping Li ; Tao Yu 等
期刊名称：Frontiers in Energy Research
电子版ISSN：2296-598X
出版年度：2021
卷号：9
DOI：10.3389/fenrg.2021.741101
语种：English
出版社：Frontiers Media S.A.
摘要：In order to improve the stability of proton exchange membrane fuel cell (PEMFC) output voltage, a data-driven output voltage control strategy based on regulation of the duty cycle of the DC-DC converter is proposed in this paper. In detail, an imitation-oriented twin delay deep deterministic (IO-TD3) policy gradient algorithm which offers a more robust voltage control strategy is demonstrated. This proposed output voltage control method is a distributed deep reinforcement learning training framework, the design of which is guided by the pedagogic concept of imitation learning. The effectiveness of the proposed control strategy is experimentally demonstrated.