Yildiz, RecepBarut, MuratZerdali, Emrah2024-11-072024-11-0720201551-32031941-0050https://doi.org/10.1109/TII.2020.2964876https://hdl.handle.net/11480/14446In this article, the real-time comparison of extended and unscented Kalman filter algorithms, which estimate the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque including viscous friction term, are performed under different operating conditions for speed-sensorless control applications of induction motors (IMs). Thus, it is clarified which algorithm is more suitable for state and parameter (load torque) estimation problem of IMs. For this purpose, four different real-time experimental tests have been carried out, which examine the effect of noise covariance matrices, parameter changes, sampling time, and computational burdens on estimation performance of both algorithms. Unlike the current literature, remarkable comparison results have been obtained.eninfo:eu-repo/semantics/closedAccessKalman filtersStatorsEstimationCovariance matricesInformaticsRotorsTorqueExtended Kalman filter (EKF)induction motor (IM)speed-sensorless controlstate estimationunscented Kalman filter (UKF)Article16106423643210.1109/TII.2020.29648762-s2.0-85087835241Q1WOS:000545243500020Q1