A Novel Deep Deterministic Policy Gradient Assisted Learning-Based Control Algorithm for Three-Phase DC/AC Inverter With an RL Load
IEEE Journal of Emerging and Selected Topics in Power Electronics(2023)
摘要
This article proposes a novel deep deterministic policy gradient (DDPG) assisted integral reinforcement learning (IRL)-based control algorithm for the three-phase dc/ac inverter feeding a resistive–inductive (RL) load. The proposed controller autonomously updates its control gains online without the need to know the system model. Excellent steady-state and dynamic system responses are achieved by the proposed control algorithm with reasonably low computational complexity. Moreover, the important initial stabilizing control problem is solved through offline training that uses the DDPG technique. Details of the DDPG-based training procedures are presented. Experimental results are presented to verify the efficacy of the proposed IRL-based control method.
更多查看译文
关键词
DC/AC inverter,deep deterministic policy gradient (DDPG),optimal control,reinforcement learning,three-phase
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要