Speed sensorless Field Oriented Control for saturated induction motor using extended Kalman filter

2011 ◽  
Author(s):  
Tahar Djellouli ◽  
Samir Moulahoum ◽  
M Seghir Boucherit ◽  
Nadir Kabache
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Field Oriented Control ◽  
Speed Sensorless

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

2017 ◽  
Vol 40 (13) ◽  
pp. 3884-3898 ◽  
Author(s):  
Ridvan Demir ◽  
Murat Barut
Keyword(s):  
Motor Control ◽  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Adaptive System ◽  
Speed Sensorless ◽  
Model Reference ◽  
Model Reference Adaptive System ◽  
Model Reference Adaptive ◽  
Hybrid Estimator

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 ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, rotor angular velocity ([Formula: see text]), load torque ([Formula: see text]) and [Formula: see text], while the AP-MRAS provides the online [Formula: see text] 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 [Formula: see text] 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.

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