微特电机 ›› 2026, Vol. 54 ›› Issue (6): 63-69.

• 驱动控制 • 上一篇    下一篇

永磁伺服电机转矩波动谐波抑制

李洪涛1,倪素娟2   

  1. 1. 中核矿业科技集团有限公司,石家庄 050000; 2. 国网河北省电力公司检修分公司,石家庄 050000
  • 出版日期:2026-06-26 发布日期:2026-06-28
  • 作者简介:李洪涛( 1984—) ,男,硕士,高级工程师,主要研究方向为直线电磁驱动。 倪素娟( 1984—) ,女,研究生,高级工程师,主要研究方向为电力系统及自动化。
  • 基金资助:
    国家自然科学基金项目( 52477035)

Harmonic Suppression of Torque Fluctuation in Permanent Magnet Servo Motor

LI Hongtao1,NI Sujuan2   

  1. 1. China Nuclear Mining Science and Technology Corporation,Shijiazhuang 050000,China;
    2. Maintenance Branch of State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,China
  • Online:2026-06-26 Published:2026-06-28

摘要: 针对永磁伺服电机周期性转矩波动抑制问题,提出一种融合谐波前馈与自适应迭代学习的混合控制策略。 该方法通过带遗忘机制的递推最小二乘法在线辨识转矩谐波的时变幅值与相位,并依据此生成具有动态相位补偿功能的前馈电压;设计增益可调的自适应迭代学习律,对前馈补偿后的残余周期性误差进行渐进修正。 基于功率为 400W 永磁同步电机平台的实验结果表明,该方法在转速为 100 r/min 空载稳态下,将转矩波动峰峰值自传统比例积分控制的 12. 8%降低至 4. 2%,主要 6 次谐波幅值衰减 23. 6 dB;在负载阶跃扰动下,系统速度恢复时间缩短约43%。 该混合策略通过在线辨识、前馈补偿与迭代学习三者的闭环协同,实现了对时变周期性扰动深度且鲁棒的抑制。

关键词: 永磁伺服电机, 转矩波动, 谐波抑制, 迭代学习控制, 前馈补偿

Abstract: Aiming at suppressing periodic torque ripple in permanent magnet synchronous servo motors,a hybrid control strategy integrating harmonic feedforward with adaptive iterative learning is proposed. This method employs a recursive leastsquares algorithm with a forgetting factor to identify the time-varying amplitudes and phases of torque harmonics online, based on which a feedforward voltage with dynamic phase compensation is generated. Concurrently, an adaptive iterative learning law with adjustable gain is designed to progressively correct the residual periodic errors after feedforward compensation. Experimental results on a power of 400 W permanent magnet servo motor platform demonstrate that the proposed method reduces the peak-to-peak torque ripple from 12. 8% ( under traditional proportional-integral control) to 4. 2% under speed 100 r/min no-load steady-state conditions,attenuates the 6th harmonic amplitude by 23. 6 dB,and shortens the speed recovery time by approximately 43% under step load disturbances. The closed-loop synergy among online identification, feedforward compensation,and iterative learning enables deep and robust suppression of time-varying periodic disturbances.

Key words: permanent magnet servo motor, torque ripple, harmonic suppression, iterative learning control, feedforward compensation

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