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A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters

Author

Listed:
  • Yao Li

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Lin Qiu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xing Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jien Ma

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jian Zhang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Youtong Fang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

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.

Suggested Citation

  • Yao Li & Lin Qiu & Xing Liu & Jien Ma & Jian Zhang & Youtong Fang, 2023. "A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters," Energies, MDPI, vol. 16(5), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2146-:d:1077335
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    References listed on IDEAS

    as
    1. Paola Maidana & Christian Medina & Jorge Rodas & Edgar Maqueda & Raúl Gregor & Pat Wheeler, 2022. "Sliding-Mode Current Control with Exponential Reaching Law for a Three-Phase Induction Machine Fed by a Direct Matrix Converter," Energies, MDPI, vol. 15(22), pages 1-17, November.
    2. Sergio Toledo & David Caballero & Edgar Maqueda & Juan J. Cáceres & Marco Rivera & Raúl Gregor & Patrick Wheeler, 2022. "Predictive Control Applied to Matrix Converters: A Systematic Literature Review," Energies, MDPI, vol. 15(20), pages 1-30, October.
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    Cited by:

    1. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.

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