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 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 11
  • Publication Date: 2012
  • Doi Number: 10.1109/tie.2011.2178209
  • 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


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.