A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters
A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters
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DOI：
10.3390/en16052146
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发表时间：
2023
期刊：
影响因子：
3.2
通讯作者：
Youtong Fang
中科院分区：
文献类型：
--
作者：
Yao Li;Lin Qiu;Xing Liu;Jien Ma;Jian Zhang;Youtong Fang
This paper presents the first approach to a current control problem for the direct matrix converter (DMC), which makes use of the deep reinforcement learning algorithm. The main objective of this paper is to solve the real-time capability issues of traditional control schemes (e.g., finite-set model predictive control) while maintaining feasible control performance. Firstly, a deep Q-network (DQN) algorithm is utilized to train an agent, which learns the optimal control policy through interaction with the DMC system without any plant-specific knowledge. Next, the trained agent is used to make computationally efficient online control decisions since the optimization process has been carried out in the training phase in advance. The novelty of this paper lies in presenting the first proof of concept by means of controlling the load phase currents of the DMC via the DQN algorithm to deal with the excessive computational burden. Finally, simulation and experimental results are given to demonstrate the effectiveness and feasibility of the proposed methodology for DMCs.
