Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors

Barut M., Demir R., Zerdali E., Inan R.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol.59, no.11, pp.4197-4206, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 11
  • Publication Date: 2012
  • Doi Number: 10.1109/tie.2011.2178209
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.4197-4206
  • Keywords: Extended Kalman filter, induction motors (IMs), load torque estimation, rotor and stator resistance estimation, sensorless control, STATOR RESISTANCE ESTIMATION, VECTOR CONTROL, ROTOR RESISTANCE, DRIVES, FLUX, EKF, IDENTIFICATION, OBSERVERS, MACHINES, SCHEME
  • Kayseri University Affiliated: No


This paper presents the real-time implementation of a bi input-extended Kalman filter (EKF) (BI-EKF)-based estimator in order to overcome the simultaneous estimation problem of the variations in stator resistance R-s and rotor resistance R-r' aside from the load torque t(L) and all states required for the speed-sensorless control of induction motors (IMs) in the wide speed range. BI-EKF algorithm consists of a single EKF algorithm using consecutively two inputs based on two extended IM models developed for the simultaneous estimation of R-r' and R-s. Therefore, from the point of real-time implementation, it requires less memory than previous EKF-based studies exploiting two separate EKF algorithms for the same aim. By using the measured stator phase voltages and currents, the developed estimation algorithm is tested with real-time experiments under challenging variations of R-s, R-r', and t(L) in a wide speed range; the results obtained from BI-EKF reveal significant improvement in the all estimated states and parameters when compared with those of the single EKFs estimating only R-r' or R-s.