Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression
Position and Speed Estimation for BLDC Motors Using Fourier-Series Regression
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DOI：
10.23919/fusion45008.2020.9190271
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发表时间：
2020-07
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影响因子：
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通讯作者：
Ajit Basarur;Jana Mayer;Antonio Zea;U. Hanebeck
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文献类型：
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作者：
Ajit Basarur;Jana Mayer;Antonio Zea;U. Hanebeck
无刷直流电动机的控制需要高分辨率的角度位置和准确的速度信息。然而，现有的基于传感器的解决方案只能直接测量位置或速度，然后用数值方法近似另一个。在这项工作中，提出了一种通过感应电机内部永磁体的杂散磁场来同时估计这两个值的新技术。然而，实现这一目标需要解决以下两个挑战。首先，磁场和电机位置之间的关系被转速以一种非直观的方式扭曲，需要仔细建模这些依赖关系。其次，推导的模型需要考虑角位置数据本质上是周期性的，而磁场数据和角速度数据是线性的（即非周期性）。为了实现这一点，我们引入了基于傅立叶级数的两种不同的多维回归模型。这两个模型首先使用参考数据进行离线训练，然后将其用作非线性估计器（如EKF）中的测量函数进行在线估计。评估表明，这两种模式都优于最先进的技术。
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.