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

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 ( R s ) and rotor resistance ( R r ) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ( i s α and i s β ) of stator currents, the stator stationary axis components ( φ s α and φ s β ) of stator fluxes, rotor angular velocity ( ω m ), load torque ( t L ) and R r , while the AP-MRAS provides the online R s 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 22 . 87 % 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.