用Transformer实现无感电机零样本转速估计，不需重新训练。
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
- 基于Transformer的上下文学习框架，仅凭电学信号推断转速。
- 实测显示低速启动时性能优于传统卡尔曼滤波器。
- 适合电机控制中无需系统辨识的实时转速估计场景。
无传感器无刷直流电机的精确转速估计对高性能控制与监控至关重要，但传统基于模型的方法难以应对系统非线性和参数不确定性。本文提出一种基于Transformer的上下文学习框架，仅利用电学测量即可实现零样本转速估计。通过离线训练滤波器模拟电机轨迹，可在未见过的真实电机上实时推理，无需重新训练，同时保持对不同工况的适应性。实验表明，该方法在低速工况（尤其是电机启动阶段）显著优于传统基于卡尔曼滤波的估计算法。
原文摘要 · Abstract (English)
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.
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