Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives

Akin, Bilal; Orguner, Umut; Ersak, A.; Ehsani, M.

doi:10.1109/tmech.2006.882996

Cited by 62 publications

(34 citation statements)

References 23 publications

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“…The induction motor state model used by the EKF is developed in the stationary reference frame and summarized by (1) [5,14], where R is the resistance, L is the inductance, and L m is the magnetizing inductance.…”

Section: Ekf For Residualmentioning

confidence: 99%

DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

Tabbache

Benbouzid²,

Kheloui

et al. 2012

International Journal of Vehicular Technology

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This paper deals with sensor fault detection within a reconfigurable direct torque control of an induction motor-based electric vehicle. The proposed strategy concerns current, voltage, and speed sensors faults that are detected and followed by post faulttolerant control to allow the vehicle continuous operation. The proposed approach is validated through experiments on an induction motor drive and simulations on an electric vehicle using a European urban and extraurban driving cycle.

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Section: Ekf For Residualmentioning

confidence: 99%

DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

Tabbache

Benbouzid²,

Kheloui

et al. 2012

International Journal of Vehicular Technology

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“…given by equation (10). In the special case when state and measurement noise signals are additive zero mean Gaussian white noise, the numerator can be computed as follows:…”

Section: Computation Of Weightsmentioning

confidence: 99%

“…Literature concerning the implementation of UKF for the estimation of hidden states from noisy data for a three phase induction motor is rather limited and there have been a very few papers published in the literature [10][11][12]. The advantage of using the unscented transform as a nonlinear approximation by showing faster convergence of the UKF against the EKF using the example of the induction machine with poor initial estimates is very well demonstrated in [12].The reason for enhanced performance of UKF is attributed to better nonlinear approximation at each step [13].…”

Section: Introductionmentioning

confidence: 99%

Design of Derivativefree State Estimators for a Three Phase Induction Motor � A Comparative Study

Ravikumar¹,

Subramanian²,

Prakash³

2011

IJCA

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Particle filters are an alternative to approximate the Kalman filter for nonlinear problems. This paper intends to assess the potential of Particle Filter (PF) and its variants in the context of the state estimation problem of a three phase induction motor. The conventional Particle Filter (SIR-PF), and particle filters that employ importance sampling through proposal distributions such as Particle Filter with Extended Kalman Filter (PF-EKF) and Particle Filter with Unscented Kalman Filter (PF-UKF), which are proposed in the literature within the particle filtering framework that takes into account of the latest observational information to reduce the risk of weight degeneracy is described and the error behaviour is analyzed through Monte Carlo simulations with regard to three scenarios Viz., low speed operation, step changes in load torque and reversal of speed. Simulation results demonstrate the superior tracking performance of PF-EKF at the expense of higher computational effort over the other approaches and can be determined to be a good substitute for the UKF in terms of accuracy of the state vector estimation.

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“…Literatürde ASM'lerin hız-algılayıcısız kontrolü için durum ve/veya parametre kestiriminin gerçekleştirildiği açık-çevrim kestiriciler [1], modele uyarlamalı gözlemleyiciler (Model reference adaptive systems) [2], tamdereceli gözlemleyiciler [3], kayma-kipli gözlemleyiciler (Sliding-mode observers) [4], Luenberger gözlemleyicisi [5] ve Kalman filtresi (KF) [6,7] vb. yöntemler önerilmiştir.…”

Section: Introductionunclassified

Hiz-Algilayicisiz Asenkron Motor Kontrolü İçi̇n Dağilimli Kalman Fi̇ltresi̇ İle Gerçek-Zamanli Yük Momenti̇ Ve Rotor Di̇renci̇ Kesti̇ri̇mi̇

Barut¹,

Yıldız²,

Zerdali³

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZBu çalışmada, asenkron motorların (ASM'lerin) hız-algılayıcısız kontrolü için ölçülen stator akımları ve gerilimleri kullanılarak; stator akımının ve rotor akısının duran eksen takımı bileşenlerini, rotor hızını, yük momentini ve rotor direncini eş zamanlı olarak kestiren dağılımlı Kalman filtresi (DKF) tabanlı kestiricinin gerçek-zamanlı uygulaması literatüre ilk kez tanıtılmaktadır. Böylece tasarlanan DKF tabanlı kestiricinin kestirim başarımı, stator gerilimleri/akımlarının ölçme hatalarını dikkate alarak, yük momenti ve rotor direncinin basamak biçimli ve doğrusal olmayan belirsizlikleri/değişimleri altında sıfır hız geçişlerini ve hız terslendirmelerini de içeren geniş bir hız bölgesi için gerçek-zamanlı olarak test edilmiş olmaktadır. Mevcut literatüre göre hız-algılayıcısız olarak en fazla durum ve parametre kestirimi yapan DKF algoritmasından elde edilen sonuçlar, kestirilen durum ve parametrelerin hızlı bir şekilde gerçek değerlerine yakınsadığını ve sürekli haldeki kestirim hatalarının oldukça tatmin edici olduğunu göstermektedir. Bu yönleri ile literatürde bilinen ilk çalışmadır.Anahtar Kelimeler: Asenkron motor, dağılımlı kalman filtresi, durum ve parametre kestirimi, hız-algılayıcısız kontrol UNSCENTED KALMAN FILTER BASED REAL-TIME LOAD TORQUE AND ROTOR RESISTANCE ESTIMATIONS FOR SPEED-SENSORLESS CONTROL OF INDUCTION MOTOR ABSTRACTIn this study, the real-time application of unscented Kalman filter (UKF) based estimator simultaneously estimating the stator stationary axis components of the stator currents and the rotor fluxes, the rotor mechanical speed, the load torque, and the rotor resistance by using measured stator currents and voltages for speedsensorless control of induction motors (IMs) is firstly introduced to the literature. Thus, the performance of proposed UKF based estimator is tested by considering measurement errors of stator voltages/currents in realtime for a wide range of speeds including zero-speed transitions and speed reversal under step-type and nonlinear uncertainties/variations of the load torque and rotor resistance. According to the current literature, the results obtained from the UKF algorithm, which estimates the maximum states and parameters in case of speedsensorless, show that estimated states and parameters quickly converge to real values and steady-state estimation errors are quite satisfactory. With these aspects, it is the first known work in the literature.

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Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives

Cited by 62 publications

References 23 publications

DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle

Design of Derivativefree State Estimators for a Three Phase Induction Motor � A Comparative Study

Hiz-Algilayicisiz Asenkron Motor Kontrolü İçi̇n Dağilimli Kalman Fi̇ltresi̇ İle Gerçek-Zamanli Yük Momenti̇ Ve Rotor Di̇renci̇ Kesti̇ri̇mi̇

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