Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (5): 66-71.doi: 10.16180/j.cnki.issn1007-7820.2020.05.011

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State Estimation for Permanent Magnet Synchronous Motor Based on Adaptive Interpolation Extended Kalman Filter

ZHU Jun,LI Zihao,LIU Bingchen,MENG Xiangbin,ZHANG Zhe   

  1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China
  • Received:2019-03-14 Online:2020-05-15 Published:2020-06-02
  • Supported by:
    National Natural Science Foundation of China(U1504506);Special Funds Subsidy Project for Basic Scientific Research Business Fees in Colleges and Universities in Henan Province(NSFRF140115)


Speed and rotor position estimation are critical for PMSM non-sensing control system. The EKF algorithm has been widely used in the industrial field as a sensorless control technology. However, the EKF algorithm produces truncation errors during system linearization, and accurate estimates cannot be obtained for highly nonlinear models. In order to reduce the error caused by nonlinear problem of EKF algorithm, based on AIEKF, a state estimation for PMSM was proposed in the study. Based on the degree of nonlinearity of the quantized state equation, this method reduced the influence of the error generated in the linearization of EKF algorithm on the estimation accuracy by adding pseudo state value, thereby reducing the errors caused by the linearization process of the system. Finally, simulation showed that the interception error of AIEKW was lower than EKF by 55.6%.

Key words: permanent magnet synchronous motor, extendedkalman filtering algorithm, truncationerrors, highly nonlinear, adaptive interpolation, estimation accuracy

CLC Number: 

  • TN787