Extended Kalman filter based estimations for improving speed-sensored control performance of induction motors

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Yildiz R., Barut M., Demir R.

IET ELECTRIC POWER APPLICATIONS, vol.14, no.12, pp.2471-2479, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 12
  • Publication Date: 2020
  • Doi Number: 10.1049/iet-epa.2020.0319
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2471-2479
  • Keywords: Kalman filters, friction, angular velocity control, stators, nonlinear filters, induction motors, sensorless machine control, machine control, torque control, rotors, speed-sensor control performance, induction motors, extended Kalman filter-based estimation algorithm, EKF-based estimation algorithm, stator stationary, rotor fluxes, rotor angular speed, load torque, rotor resistance, magnetising inductance, single EKF algorithm, measured rotor speed, stationary axis components, measured stator currents, measured priori estimation values, posterior estimation ones, eighth order EKF algorithm, wide-speed range, developed sixth order EKF, additional estimations, improved estimations, ROTOR RESISTANCE, PARAMETER, OBSERVERS, STATOR, MRAS, FLUX, EKF
  • Kayseri University Affiliated: No


In this study, an extended Kalman filter (EKF)-based estimation algorithm is presented to improve the speed-sensored control performance of induction motors (IMs). The proposed EKF-based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed-sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real-time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real-time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.