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In this study, an extended Kalman filter (EKF)‐based estimation algorithm is presented to improve the speed‐sensored control performance of induction motors (IMs). The proposed EKF‐based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed‐sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real‐time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real‐time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.

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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.

Bu çalışmada, asenkron motorların alan zayıflama bölgesindeki yüksek başarımlı hız-algılayıcılı kontrolü için yeni bir indirgenmiş dereceli genişletilmiş Kalman filtresi tabanlı kestirici tasarlanarak benzetim ve gerçek-zamanlı deneylerle test edilmektedir. Önerilen indirgenmiş dereceli genişletilmiş Kalman filtresi ile vektör kontrol sistemi için gerekli olan rotor akısının stator duran eksen takımı bileşenleri kestirilmektedir. Ayrıca önerilen algoritma ile akı kestirimlerine ek olarak değeri çalışma koşulları ile değişen rotor direnci ve mıknatıslama endüktansı, sıfır hız ve alan zayıflama bölgesini de içeren geniş bir hız aralığında eş-zamanlı olarak kestirilmektedir. Önerilen kestirim algoritmasına ait benzetim sonuçları hız, yük momenti, rotor direnci ve mıknatıslama endüktansının zorlayıcı değişimleri altında oldukça tatmin edicidir. Bu nedenle önerilen kestirim algoritmasını kullanan doğrudan vektör kontrollü asenkron motor sürücüsünün başarımı da oldukça iyi olmaktadır. Ayrıca, elde edilen gerçek-zamanlı kestirim sonuçları da önerilen kestiricinin başarımını onaylamaktadır. In this study, for a high performance speed-sensored control in field weakening region of induction motors, a novel reduced order extended Kalman filter based estimator is designed and tested by experiments performed in simulation and real-time experiments. The proposed reduced order extended Kalman filter estimates the stator stationary axis components of rotor fluxes, which are required for vector control system. In addition to the flux estimations, rotor resistance and mutual inductance whose values vary according to operating conditions of induction motors are estimated simultaneously in a wide speed range including zero-speed and field-weakening. The simulation based estimation results associated with proposed estimation algorithm are quite satisfactory under challenging variations of rotor angular velocity, load torque, rotor resistance, and mutual inductance. Therefore, the performance of the direct vector controlled induction motor drive using the proposed estimation algorithm also becomes quite well. Moreover, the estimation results in real-time experiments also confirm the performances of the proposed estimator.