Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives

Cristian Lascu, Saeed Jafarzadeh, M.Sami Fadali

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

33 Citationer (Scopus)

Resumé

This paper investigates the application, design, and
implementation of the square root unscented Kalman filter (UKF)
(SRUKF) for induction motor (IM) sensorless drives. The UKF
uses nonlinear unscented transforms (UTs) in the prediction step
in order to preserve the stochastic characteristics of a nonlinear
system. The advantage of using the UT is its ability to capture
the nonlinear behavior of the system, unlike the extended Kalman
filter (EKF) that uses linearized models. The SRUKF implements
the UKF using square root filtering to reduce computational errors.
We discuss the theoretical aspects and implementation details
of the SRUKF for IM drives. Experimental results for a directtorque-
controlled drive are presented for a wide speed range of
operation, with focus on low-speed performance. A comparison
with the conventional EKF and the UKF is included. Our results
show that the SRUKF is a viable and powerful tool for IM state
estimation.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Industry Applications
Vol/bind49
Udgave nummer1
Sider (fra-til)92-99
Antal sider8
ISSN0093-9994
DOI
StatusUdgivet - 2013

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State estimation
Kalman filters
Induction motors

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title = "Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives",
abstract = "This paper investigates the application, design, andimplementation of the square root unscented Kalman filter (UKF)(SRUKF) for induction motor (IM) sensorless drives. The UKFuses nonlinear unscented transforms (UTs) in the prediction stepin order to preserve the stochastic characteristics of a nonlinearsystem. The advantage of using the UT is its ability to capturethe nonlinear behavior of the system, unlike the extended Kalmanfilter (EKF) that uses linearized models. The SRUKF implementsthe UKF using square root filtering to reduce computational errors.We discuss the theoretical aspects and implementation detailsof the SRUKF for IM drives. Experimental results for a directtorque-controlled drive are presented for a wide speed range ofoperation, with focus on low-speed performance. A comparisonwith the conventional EKF and the UKF is included. Our resultsshow that the SRUKF is a viable and powerful tool for IM stateestimation.",
keywords = "Induction machine drives, Kalman filters, sensorless drives, state estimation",
author = "Cristian Lascu and Saeed Jafarzadeh and M.Sami Fadali",
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Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives. / Lascu, Cristian; Jafarzadeh, Saeed; Fadali, M.Sami.

I: I E E E Transactions on Industry Applications, Bind 49, Nr. 1, 2013, s. 92-99.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives

AU - Lascu, Cristian

AU - Jafarzadeh, Saeed

AU - Fadali, M.Sami

PY - 2013

Y1 - 2013

N2 - This paper investigates the application, design, andimplementation of the square root unscented Kalman filter (UKF)(SRUKF) for induction motor (IM) sensorless drives. The UKFuses nonlinear unscented transforms (UTs) in the prediction stepin order to preserve the stochastic characteristics of a nonlinearsystem. The advantage of using the UT is its ability to capturethe nonlinear behavior of the system, unlike the extended Kalmanfilter (EKF) that uses linearized models. The SRUKF implementsthe UKF using square root filtering to reduce computational errors.We discuss the theoretical aspects and implementation detailsof the SRUKF for IM drives. Experimental results for a directtorque-controlled drive are presented for a wide speed range ofoperation, with focus on low-speed performance. A comparisonwith the conventional EKF and the UKF is included. Our resultsshow that the SRUKF is a viable and powerful tool for IM stateestimation.

AB - This paper investigates the application, design, andimplementation of the square root unscented Kalman filter (UKF)(SRUKF) for induction motor (IM) sensorless drives. The UKFuses nonlinear unscented transforms (UTs) in the prediction stepin order to preserve the stochastic characteristics of a nonlinearsystem. The advantage of using the UT is its ability to capturethe nonlinear behavior of the system, unlike the extended Kalmanfilter (EKF) that uses linearized models. The SRUKF implementsthe UKF using square root filtering to reduce computational errors.We discuss the theoretical aspects and implementation detailsof the SRUKF for IM drives. Experimental results for a directtorque-controlled drive are presented for a wide speed range ofoperation, with focus on low-speed performance. A comparisonwith the conventional EKF and the UKF is included. Our resultsshow that the SRUKF is a viable and powerful tool for IM stateestimation.

KW - Induction machine drives

KW - Kalman filters

KW - sensorless drives

KW - state estimation

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EP - 99

JO - I E E E Transactions on Industry Applications

JF - I E E E Transactions on Industry Applications

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