微特电机 ›› 2022, Vol. 50 ›› Issue (5): 62-67.

• 驱动控制 • 上一篇    下一篇

基于卡尔曼滤波器的永磁同步电机自抗扰控制

朱德明1, 张军1, 白晨光2   

  1. 1.南京电子技术研究所,南京 210039;
    2.南京师范大学 电气与自动化工程学院,南京 210046
  • 收稿日期:2021-12-23 出版日期:2022-05-28 发布日期:2022-06-22
  • 作者简介:朱德明(1980—),男,博士,研究方向为伺服系统驱动控制。
  • 基金资助:
    江苏省研究生实践创新项目(SJCX21_0562,SJCX21_0566,SJCX21_0568)

Active Disturbance Rejection Control with Kalman Filter for Permanent Magnet Synchronous Motor

ZHU Deming1, ZHANG Jun1, BAI Chenguang2   

  1. 1. Nanjing Research Institute of Electronics Technology,Nanjing 210039, China;
    2. School of Electrical and Automation Engineering,Nanjing Normal University, Nanjing 210046,China
  • Received:2021-12-23 Online:2022-05-28 Published:2022-06-22

摘要: 针对外部突加大负载扰动时,传统自抗扰控制策略中扩展状态观测器(ESO)的观测精度差,以及系统噪声对ESO观测精度的影响,提出一种基于卡尔曼滤波器的自抗扰控制策略(KF-ADRC)。通过卡尔曼滤波器观测出的负载转矩进行前馈补偿,以提高大负载扰动下ESO观测精度;通过卡尔曼滤波器观测出的转速用作系统闭环反馈,以削弱系统噪声对ESO观测精度的影响。将传统自抗扰中的线性误差状态反馈律替换为非线性误差状态反馈律,以提升系统跟踪和抗扰性能。搭建了400 W PMSM伺服系统驱动平台验证了该控制策略的有效性。

关键词: 永磁同步电机, 自抗扰控制, 扩张状态观测器, 卡尔曼滤波器, 非线性状态误差反馈律

Abstract: When the external disturbance increased abruptly, the observation accuracy of ESO could deteriorate.The system noise limited the bandwidth of ESO, which adversely affected the observation effect of ESO.A novel Kalman-filter based active disturbance rejection control (KF-ADRC) scheme was proposed. The KF-ADRC scheme used the load torque observed by KF for feed-forward compensation, which relieved the observation pressure of ESO and improved the observation effect of ESO.Using the speed observed by Kalman as closed-loop feedback made the system more efficient.The nonlinear error state feedback law(NLSEF) to further improved the system tracking and disturbance rejection performance replaced the linear error state feedback rate in the traditional ADRC. The experimental results verify the effectiveness of the proposed scheme in a 400 Wpermanent magnet synchronous motor drive system.

Key words: permanent magnet synchronous motor(PMSM), active disturbance rejection control (ADRC), extended state observer (ESO), Kalman filter (KF), nonlinear state error feedback law(NLSEF)

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