This work deals with the tuning of an Extended Kalman Filter for sensorless control of induction motors for electrical traction in automotive. Assuming that the parameters of the induction motor-load model are known, Genetic Algorithms are used for obtaining the system noise covariance matrix, considering the measurement noise covariance matrix equal to the identity matrix. It is shown that only stator currents have to be acquired for reaching this objective, which is easy to accomplish using Hall-effect transducers. In fact, the Genetic Algorithm minimizes, with respect to the system covariance matrix, a suitable measure of the displacement between the stator currents experimentally acquired and those estimated by the Kalman filter. The proposed method is validated by experiments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor




    Publication date :

    2019-07-01


    Size :

    401351 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Enhanced Sensorless Model Predictive Control of Induction Motor Based on Extended Kalman Filter

    Ahmed Ibrahim Soliman / Ahmed Farhan / Mohamed Abdelrahem et al. | BASE | 2020

    Free access


    Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter

    Mustapha Bendjima / Abdeldjebar Hazzab / Mansour Bechar et al. | BASE | 2023

    Free access

    Auto-tuning extended Kalman filters to improve state estimation

    Boulkroune, Boulaid / Geebelen, Kurt / Wan, Jia et al. | IEEE | 2023


    High-Performance Sensorless PMSM Drive With Extended Kalman Filter

    Wu, Bin / Kojori, Hassan / Shi, K. L. | SAE Technical Papers | 2002