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A Computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids

Author

Listed:
  • Ran Li

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Wendong Feng

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Tianhao Qie

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Yulin Liu

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Tyrone Fernando

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Herbert HoChing Iu

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

  • Xinan Zhang

    (Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia)

Abstract

This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method is model-free and eliminates such dependencies. By integrating a recurrent equilibrium network (REN), the controller achieves an enhanced dynamic response and robust steady-state performance, while maintaining a low computational complexity. Moreover, its closed-loop stability can be rigorously verified based on contraction theory and incremental quadratic constraints. To facilitate practical implementation, a design guideline is provided. Experimental results confirm that the proposed method outperforms conventional PI and model predictive controllers in terms of response speed, harmonic suppression, and robustness under parameter variations. Additionally, the algorithm is lightweight enough for real-time execution on embedded platforms, such as a TI DSP.

Suggested Citation

  • Ran Li & Wendong Feng & Tianhao Qie & Yulin Liu & Tyrone Fernando & Herbert HoChing Iu & Xinan Zhang, 2025. "A Computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids," Energies, MDPI, vol. 18(9), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2383-:d:1650449
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