Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00673 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909560674975744 |
|---|---|
| author | Colombo, Alessandro Busetto, Riccardo Breschi, Valentina Forgione, Marco Piga, Dario Formentin, Simone |
| author_facet | Colombo, Alessandro Busetto, Riccardo Breschi, Valentina Forgione, Marco Piga, Dario Formentin, Simone |
| contents | Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system nonlinearities and parameter uncertainties. In this work, we propose an in-context learning framework leveraging transformer-based models to perform zero-shot speed estimation using only electrical measurements. By training the filter offline on simulated motor trajectories, we enable real-time inference on unseen real motors without retraining, eliminating the need for explicit system identification while retaining adaptability to varying operating conditions. Experimental results demonstrate that our method outperforms traditional Kalman filter-based estimators, especially in low-speed regimes that are crucial during motor startup. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-Context Learning for Zero-Shot Speed Estimation of BLDC motors Colombo, Alessandro Busetto, Riccardo Breschi, Valentina Forgione, Marco Piga, Dario Formentin, Simone Systems and Control Robotics Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system nonlinearities and parameter uncertainties. In this work, we propose an in-context learning framework leveraging transformer-based models to perform zero-shot speed estimation using only electrical measurements. By training the filter offline on simulated motor trajectories, we enable real-time inference on unseen real motors without retraining, eliminating the need for explicit system identification while retaining adaptability to varying operating conditions. Experimental results demonstrate that our method outperforms traditional Kalman filter-based estimators, especially in low-speed regimes that are crucial during motor startup. |
| title | In-Context Learning for Zero-Shot Speed Estimation of BLDC motors |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2504.00673 |