基于稳态线性卡尔曼滤波和高频方波注入抑制噪声的永磁同步电机转子位置估计

J. Sun, Feng Ju, Qinghao Wang, Yan Hong, Bai Chen, Hongtao Wu, Jiangsu Hengli
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引用次数: 1

摘要

提出了一种基于稳态线性卡尔曼滤波(SSLKF)的永磁同步电动机静止和低速运行的无方波注入控制算法。在方波注入法中,通常采用锁相环(PLL)从励磁电流中提取转子位置和转速信息。但是,由于电流信号中存在测量噪声,估计的速度和位置值会有较大误差,影响系统性能。与传统方法相比,该算法对噪声具有更强的鲁棒性,即使在非常高的噪声下也具有更高的估计精度。此外,在SSLKF中引入了额外的加速前馈项,提高了系统的响应速度。仿真结果验证了该算法的性能。速度估计的均方根误差(RMSE)从180 RPM降低到30 RPM,位置估计的均方根误差(RMSE)从10°降低到5°。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rotor Position Estimation of PMSM With Noise Suppression Using Steady-State Linear Kalman Filter and High Frequency Square Wave Injection
In this paper, a new square wave injection sensorless control algorithm based on steady-state linear Kalman filter (SSLKF) is proposed for standstill and low-speed operation of permanent magnet synchronous motors (PMSM). Usually, in the square wave injection method, phase-locked loop (PLL) is used to extract the rotor position and speed information from the excited current. However, due to the measurement noise in the current signal, there will be large errors in the estimated speed and position values, which will affect the system performance. Compared with traditional methods, the proposed algorithm is more robust to noise and has higher estimation accuracy even under very high noise. In addition, an additional acceleration feedforward term is introduced into the SSLKF, which improves the response speed of the system. The performance of the proposed algorithm is evaluated in simulations. The root mean square error (RMSE) of velocity estimation is reduced from 180 RPM to 30 RPM, and RMSE of position estimation is reduced from 10° to 5°.
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