Title: In-Context Learning for Zero-Shot Speed Estimation of BLDC motors Show Chinese title

Title: 基于上下文学习的无刷直流电机零样本速度估计

Abstract: 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. Show Chinese abstract

Abstract: 无传感器无刷直流电机的精确速度估计对于高性能控制和监测至关重要，但基于传统模型的方法难以应对系统的非线性和参数不确定性。 在这项工作中，我们提出了一种利用基于Transformer的模型进行零样本速度估计的上下文学习框架，仅使用电气测量值即可完成任务。 通过在模拟电机轨迹上离线训练滤波器，我们能够在未见过的实际电机上实现实时推理，而无需重新训练，从而消除了显式系统辨识的需求，同时保留了适应不同运行条件的能力。 实验结果表明，我们的方法优于传统的基于卡尔曼滤波器的估计器，尤其是在电机启动过程中至关重要的低速条件下表现出色。