Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

Demir R., Barut M.

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, vol.40, no.13, pp.3884-3898, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 13
  • Publication Date: 2018
  • Doi Number: 10.1177/0142331217734631
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3884-3898
  • Keywords: Induction motors, sensorless control, extended Kalman filter, model reference adaptive system, parameter estimation, STATOR RESISTANCE ESTIMATION, DIRECT TORQUE CONTROL, DISTRIBUTED-PARAMETER SYSTEMS, ROTOR RESISTANCE, SIGNAL INJECTION, ENERGY-STORAGE, VECTOR CONTROL, IM DRIVES, DESIGN, ZERO
  • Kayseri University Affiliated: No


This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.