GD-Based Robust Model Predictive Control for DC-DC Converters with Inductance Identification
编号:62 访问权限:仅限参会人 更新:2025-05-06 14:57:57 浏览:8次 张贴报告

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摘要
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.
关键词
dual active bridge converter,model predictive control,gradient descent,parameter identification,robustness
报告人
Zheng Yin
PhD Southeast University

稿件作者
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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重要日期
  • 会议日期

    06月05日

    2025

    06月08日

    2025

  • 04月30日 2025

    初稿截稿日期

主办单位
IEEE PELS
IEEE
承办单位
Southeast University
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