• DocumentCode
    582285
  • Title

    The improved EKF velocity estimation algorithm for PMSM and experimental assessment

  • Author

    Dongwei, He ; Xiafu, Peng ; Xuecheng, Jiang ; Jiehua, Zhou

  • Author_Institution
    Dept. of Autom., Xiamen Univ., Xiamen, China
  • fYear
    2012
  • fDate
    25-27 July 2012
  • Firstpage
    4273
  • Lastpage
    4277
  • Abstract
    This paper presents the improved EKF rotor velocity estimation algorithm for the PMSM servo system with an incremental optical encoder in detail. In a common PMSM servo system, the velocity got by the fixed-time method always carries quantization noise in the form of random impulse due to the quantization error of the encoder. The traditional method treats the quantization error as high frequency noise, and uses a first-order low-pass filter to eliminate it, but it would bring lagging and attenuation. While the proposed improved EKF velocity estimation algorithm can overcome the defects mentioned above to estimate the velocity precisely, besides no circuit modify and no matrix calculation are need. Finally serial experiments are carried out to evaluate its performance and practicality.
  • Keywords
    Kalman filters; encoding; low-pass filters; nonlinear filters; permanent magnet motors; quantisation (signal); rotors; servomotors; synchronous motors; PMSM servo system; attenuation; experimental assessment; first-order low-pass filter; fixed-time method; high frequency noise; improved EKF rotor velocity estimation algorithm; incremental optical encoder; quantization error; quantization noise; random impulse; Acceleration; Estimation; Noise; Optical filters; Quantization; Rotors; Servomotors; Extended Kalman filter (EKF); Permanent Magnet Synchronous Motor (PMSM); Velocity estimation; incremental optical encoder;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2012 31st Chinese
  • Conference_Location
    Hefei
  • ISSN
    1934-1768
  • Print_ISBN
    978-1-4673-2581-3
  • Type

    conf

  • Filename
    6390676