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Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression

机译：基于傅里叶级数回归的BLDC电机位置和速度估计

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The control of brushless DC motors requires high-resolution angular position and accurate speed information. However, available sensor-based solutions only measure either the position or the speed directly, and then approximate the other numerically. In this work, a novel technique is presented to estimate both of these values simultaneously by sensing the stray magnetic field of the internal permanent magnets of the motor. However, achieving this requires the following two challenges to be addressed. First, the relationship between the magnetic field and the motor position is distorted by the rotational speed in a non-intuitive way, requiring careful modeling of these dependencies. Second, the derived model needs to consider that the angular position data is periodic by nature, but the magnetic field data and the angular speed data are linear (i.e., non-periodic). To achieve this, we introduce two different multidimensional regression models based on the Fourier series. Both models are first trained offline using reference data, and then used as a measurement function in a nonlinear estimator such as the EKF for online estimation. Evaluations show that both models outperform state-of-the-art techniques.
机译：无刷直流电动机的控制需要高分辨率的角位置和准确的速度信息。但是，可用的基于传感器的解决方案仅直接测量位置或速度，然后在数值上近似另一个。在这项工作中，提出了一种新颖的技术，可以通过感测电动机内部永磁体的杂散磁场来同时估计这两个值。但是，要实现这一目标，需要解决以下两个挑战。首先，磁场和电动机位置之间的关系会以非直观的方式被转速扭曲，因此需要对这些依赖关系进行仔细的建模。其次，导出的模型需要考虑角位置数据本质上是周期性的，但是磁场数据和角速度数据是线性的（即，非周期性的）。为了实现这一点，我们基于傅立叶级数引入了两个不同的多维回归模型。两种模型都首先使用参考数据进行离线训练，然后在非线性估计器（例如EKF）中用作在线估计的测量函数。评估表明，这两种模型都优于最新技术。

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