GD-Based Robust Model Predictive Control for DC-DC Converters with Inductance Identification
ID:62 View Protection:ATTENDEE Updated Time:2025-05-06 14:57:57 Hits:194 Poster

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Abstract
The voltage performance of conventional model predictive control (MPC) depends on the accuracy of leakage inductance parameters in dual active bridge (DAB) converters. To address this issue, a gradient-descent-based robust model predictive control (GD-RMPC) is proposed. By integrating the mathematical model of the DAB converter, a gradient-descent equation is established to achieve real-time online identification of the leakage inductance parameter, ensuring robust output voltage control for the DAB converter. The proposed method allows for rapid, accurate, and online identification of the leakage inductance parameter, suppressing the adverse effects of parameter mismatches on conventional MPC. Finally, a DAB converter experimental platform is established, and the effectiveness of the proposed method is validated.
Keywords
dual active bridge converter,model predictive control,gradient descent,parameter identification,robustness
Speaker
Zheng Yin
PhD Southeast University

Submission Author
Zheng Yin Southeast University
Fujin Deng Southeast University
Yaqian Zhang Southeast University
Sayed Abulanwar Mansoura University
Yifu Ren Tsinghua University
FengTao Gao Tsinghua University;Xi'an University of Technology
Garcia Cristian Universidad de Talca
Jose Rodriguez Universidad San Sebastian
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Important Date
  • Conference Date

    Jun 05

    2025

    to

    Jun 01

    2026

  • May 30 2025

    Draft paper submission deadline

  • Jun 08 2025

    Registration deadline

Sponsored By
China Southeast University
IEEE Power Electronics Society
Jiangsu Association of Automation
Nanjing Section IE Chapter
Organized By
Southeast University