微特电机 ›› 2020, Vol. 48 ›› Issue (12): 32-35.

• 驱动控制 • 上一篇    下一篇

基于AEKF的永磁同步电机转速控制方法

党克, 刘子源, 田勇, 衣鹏博, 刘闯   

  1. 东北电力大学现代电力系统仿真控制与绿色电能新技术教育部重点实验室,吉林132012
  • 收稿日期:2020-09-09 出版日期:2020-12-28 发布日期:2020-12-23
  • 作者简介:党克(1960—)男,硕士生导师,研究员,主要从事新能源发电技术、电力系统电能质量方面的研究。
  • 基金资助:
    国家重点研发计划项目(2017YFB090330)

Speed Control of Permanent Magnet Synchronous Motor Based on Adaptive Extended Kalman Filter

DANG Ke, LIU Zi-yuan, TIAN Yong, YI Peng-bo, LIU Chuang   

  1. Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of Ministry of Education, Northeast Dianli University,Jilin 132012,China
  • Received:2020-09-09 Online:2020-12-28 Published:2020-12-23

摘要: 采用扩展卡尔曼滤波(EKF)对永磁同步电机的转子位置和速度进行估计的研究方法,难以建立精确的模型,在噪声不确定的情况下无法保证估计精度,发生滤波扩散的可能性增大。研究了自适应扩展卡尔曼滤波算法(AEKF),该方法将基于改进的Sage-Husa自适应卡尔曼滤波算法和基于新息的自适应扩展卡尔曼滤波算法相结合,对系统影响较大的噪声矩阵进行实时更新。实验结果表明,AEKF控制策略相较于传统EKF算法对转子位置和转速估计精度更高,几乎没有转速超调,且算法具有良好的参数鲁棒性,提高了永磁同步电机控制系统的稳定性。

关键词: 永磁同步电机, 转速估计, 无传感器控制, 自适应扩展卡尔曼滤波

Abstract: The method for researching the rotor position and speed estimation of permanent magnet synchronous motor was extended Kalman filter (EKF). This method was difficult to establish an accurate model, and the estimation accuracy could not be guaranteed under the condition of uncertain noise. The possibility of filter diffusion was increased. The adaptive extended Kalman filter algorithm (AEKF) was proposed, which combined the improved Sage-Husa adaptive Kalman filter algorithm with the innovation-based adaptive extended Kalman filter algorithm to achieve the impact on the system. The larger noise matrix was updated in real time. The experimental results showed that the proposed AEKF control strategy had higher estimation accuracy of rotor position and speed than the traditional EKF algorithm, almost no speed overshoot, and the algorithm had good parameter robustness,which improved the stability of the PMSM sensorless control system.

Key words: permanent magnet synchronous motor(PMSM), speed estimation, sensorless control, adaptive extended Kalman filter (AEKF)

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