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

Ran Li, Wendong Feng, Tianhao Qie, Yulin Liu, Tyrone Fernando, Herbert HoChing Iu () and Xinan Zhang ()
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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

Energies, 2025, vol. 18, issue 9, 1-15

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.

Keywords: three-phase AC/DC converter; learning-based control; low computational complexity; stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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