Learning and Current Prediction of PMSM Drive via Differential Neural Networks
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910742685417472 |
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| author | Mei, Wenjie Wang, Xiaorui Lu, Yanrong Yu, Ke Li, Shihua |
| author_facet | Mei, Wenjie Wang, Xiaorui Lu, Yanrong Yu, Ke Li, Shihua |
| contents | Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems, specifically permanent magnet synchronous motors (PMSMs), and to predict their current trajectories. The efficacy of our approach is validated through experiments conducted under various load disturbances and no-load conditions. The results demonstrate that our method effectively and accurately reconstructs the original systems, showcasing strong short-term and long-term prediction capabilities and robustness. This study provides valuable insights into learning the inherent dynamics of complex dynamical data and holds potential for further applications in fields such as weather forecasting, robotics, and collective behavior analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09028 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning and Current Prediction of PMSM Drive via Differential Neural Networks Mei, Wenjie Wang, Xiaorui Lu, Yanrong Yu, Ke Li, Shihua Machine Learning Systems and Control Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems, specifically permanent magnet synchronous motors (PMSMs), and to predict their current trajectories. The efficacy of our approach is validated through experiments conducted under various load disturbances and no-load conditions. The results demonstrate that our method effectively and accurately reconstructs the original systems, showcasing strong short-term and long-term prediction capabilities and robustness. This study provides valuable insights into learning the inherent dynamics of complex dynamical data and holds potential for further applications in fields such as weather forecasting, robotics, and collective behavior analysis. |
| title | Learning and Current Prediction of PMSM Drive via Differential Neural Networks |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2412.09028 |