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Dual Active Bridge (DAB) converters are widely employed in DC microgrids. However, conventional Model Predictive Control (MPC) methods suffer from a strong dependence on accurate model parameters. To address this limitation, this paper proposes a Kalman Observer-based Model-Free Predictive Voltage Control (KO-MFPVC) strategy. Firstly, the impact of parameter mismatch on the performance of conventional MPC is thoroughly analyzed. An ultra-local model of the DAB converter is constructed, and system description is simplified via local linearization, thereby reducing the controller’s reliance on precise mathematical models. Secondly, a Kalman observer is designed to estimate and compensate for lumped disturbances in real-time. By integrating dynamic matrix updating and adaptive gain adjustment, the system’s disturbance rejection capability under parameter mismatch and noise is enhanced. Finally, a cost function is designed to solve for the optimal phase-shift ratio, achieving model-free predictive voltage control. Experimental results demonstrate that, compared to conventional MPC, the proposed scheme significantly enhances the converter’s robustness against model mismatch and operational variations while maintaining favorable dynamic response characteristics.

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Model-Free Predictive Voltage Control of Dual Active Bridge Converter Based on Kalman Observer

  • 会议论文

  • 发表日期：

    2026年

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  • Shengwei Gao,
  • Xiaofeng Liu,
  • Fangze Di

摘要

Dual Active Bridge (DAB) converters are widely employed in DC microgrids. However, conventional Model Predictive Control (MPC) methods suffer from a strong dependence on accurate model parameters. To address this limitation, this paper proposes a Kalman Observer-based Model-Free Predictive Voltage Control (KO-MFPVC) strategy. Firstly, the impact of parameter mismatch on the performance of conventional MPC is thoroughly analyzed. An ultra-local model of the DAB converter is constructed, and system description is simplified via local linearization, thereby reducing the controller’s reliance on precise mathematical models. Secondly, a Kalman observer is designed to estimate and compensate for lumped disturbances in real-time. By integrating dynamic matrix updating and adaptive gain adjustment, the system’s disturbance rejection capability under parameter mismatch and noise is enhanced. Finally, a cost function is designed to solve for the optimal phase-shift ratio, achieving model-free predictive voltage control. Experimental results demonstrate that, compared to conventional MPC, the proposed scheme significantly enhances the converter’s robustness against model mismatch and operational variations while maintaining favorable dynamic response characteristics.