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

作     者：Li, Ran Feng, Wendong Qie, Tianhao Liu, Yulin Fernando, Tyrone Iu, Herbert HoChing Zhang, Xinan 

作者机构：Univ Western Australia Dept Elect Elect & Comp Engn Perth WA 6009 Australia 

出 版 物：《ENERGIES》 (Energies)

年 卷 期：2025年第18卷第9期

页      面：2383-2383页

核心收录：

基　　金：Australian Future Battery Industries Corporate Research Center "Mine Electrification" project BU/PG: 00660/55005100 

主　　题：three-phase AC/DC converter learning-based control low computational complexity stability 

摘      要：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.