A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive.

中文翻译：

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同步磁阻电机无传感器控制的滞后补偿增强自适应准衰落卡尔曼滤波器

本文针对同步磁阻电机（SynRM）无传感器驱动提出了一种基于滞后补偿辅助自适应准衰落卡尔曼滤波器（LC-AQFKF）的位置估计策略。在LC-QFKF中，导出准衰落因子避免了传统自适应衰落卡尔曼滤波器的苛刻假设，在保证估计精度的同时提高了方法的可访问性。通过将准衰落因子引入预测误差协方差矩阵，提高了 LC-AQFKF 的计算效率。此外，分析了基于AQFKF的有源反电动势观测器的频率特性，克服了AQFKF的低通滤波特性导致的高速情况下的相位滞后问题。这样，转子位置的估计精度显着提高。此外，提出了双动态位置补偿方法，以增强动态条件下无传感器SynRM驱动器的位置估计性能。建议方案的有效性在 1.5 kW SynRM 驱动器上得到验证。