DT-MPC: Synthesizing Derivation-Free Model Predictive Control from Power Converter Netlists via Physics-Informed Neural Digital Twins
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911316581548032 |
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| author | Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Wheeler, Patrick Vazquez, Sergio Franquelo, Leopoldo G. |
| author_facet | Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Wheeler, Patrick Vazquez, Sergio Franquelo, Leopoldo G. |
| contents | Model Predictive Control (MPC) is a powerful control strategy for power electronics, but it highly relies on manually-derived and topology-specific analytical models, which is labor-intensive and time-consuming in practical designs. To overcome this bottleneck, this paper introduces a Digital-Twin-based MPC (DT-MPC) framework for generic power converters that can systematically translate a high-level circuit into an objective-aware control policy by leveraging a DT as a high-fidelity system model. Furthermore, a physics-informed neural surrogate predictor is proposed to accelerate predictions by DT and enable real-time operation. A gradient-free simplex search optimizer is also introduced to efficiently handle complex multi-objective optimization. The efficacy of the framework has been validated through a cloud-to-edge deployment on a 1500 W dual active bridge converter. Experimental results show that the synthesized predictive model achieves an inference speed over 7 times faster than real time, the DT-MPC controller outperforms several human-designed counterparts, and the overall framework reduces engineering design time by over 95\%, verifying the superiority of DT-MPC on generalized power converters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DT-MPC: Synthesizing Derivation-Free Model Predictive Control from Power Converter Netlists via Physics-Informed Neural Digital Twins Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Wheeler, Patrick Vazquez, Sergio Franquelo, Leopoldo G. Systems and Control Model Predictive Control (MPC) is a powerful control strategy for power electronics, but it highly relies on manually-derived and topology-specific analytical models, which is labor-intensive and time-consuming in practical designs. To overcome this bottleneck, this paper introduces a Digital-Twin-based MPC (DT-MPC) framework for generic power converters that can systematically translate a high-level circuit into an objective-aware control policy by leveraging a DT as a high-fidelity system model. Furthermore, a physics-informed neural surrogate predictor is proposed to accelerate predictions by DT and enable real-time operation. A gradient-free simplex search optimizer is also introduced to efficiently handle complex multi-objective optimization. The efficacy of the framework has been validated through a cloud-to-edge deployment on a 1500 W dual active bridge converter. Experimental results show that the synthesized predictive model achieves an inference speed over 7 times faster than real time, the DT-MPC controller outperforms several human-designed counterparts, and the overall framework reduces engineering design time by over 95\%, verifying the superiority of DT-MPC on generalized power converters. |
| title | DT-MPC: Synthesizing Derivation-Free Model Predictive Control from Power Converter Netlists via Physics-Informed Neural Digital Twins |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.12026 |