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spelling doaj-dece9c8eacba44f1be4c8fb8510133bc2021-04-02T11:17:14ZengWileyThe Journal of Engineering2051-33052019-04-0110.1049/joe.2018.8081JOE.2018.8081Sensorless control of PMSM using an adaptively tuned SCKFGulur Raghavendra Gopinath0Prasad Das Shyama1Department of Electrical Engineering, Indian Institute of Technology, Kanpur, Uttar PradeshDepartment of Electrical Engineering, Indian Institute of Technology, Kanpur, Uttar PradeshThis study reports the application of an adaptively tuned square-root Cubature Kalman filter (SCKF) for the speed and position estimation of a permanent magnet synchronous motor (PMSM) drive. The proposed estimator is observed to exhibit improved noise rejection characteristics as compared to the hitherto widely applied extended Kalman filter (EKF) observer. A third degree spherical–radial cubature rule is used in the Cubature Kalman filter (CKF) to numerically compute the multivariate moment integrals of the general Bayesian estimation equation. CKF is a non-linear filter which avoids linearisation and the associated errors. The realisation of CKF using the square-root algorithm results in numerical stability, as with the realisation of EKF using the square-root algorithm. Simulation results are presented for a three-phase inverter-fed PMSM, along with the experimental results. The estimator and the control algorithms are realised on the MATLAB real-time environment, interfaced with the hardware using the National Instruments data acquisition system NI PCI-6221.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8081numerical stabilitysynchronous motor drivespermanent magnet motorsKalman filtersnonlinear filtersBayes methodssensorless machine controlinvertorsdata acquisitionNI PCI-6221National Instruments data acquisition systemsquare-root algorithmadaptively tuned square-root cubature Kalman filterPMSMsensorless controlspeed estimationthird degree spherical-radial cubature ruleposition estimationadaptively tuned SCKFcontrol algorithmsthree-phase inverter-fed PMSMnumerical stabilitynonlinear filtergeneral Bayesian estimation equationmultivariate moment integralsCKFimproved noise rejection characteristicspermanent magnet synchronous motor drive
collection DOAJ
language English
format Article
sources DOAJ
author Gulur Raghavendra Gopinath
Prasad Das Shyama
spellingShingle Gulur Raghavendra Gopinath
Prasad Das Shyama
Sensorless control of PMSM using an adaptively tuned SCKF
The Journal of Engineering
numerical stability
synchronous motor drives
permanent magnet motors
Kalman filters
nonlinear filters
Bayes methods
sensorless machine control
invertors
data acquisition
NI PCI-6221
National Instruments data acquisition system
square-root algorithm
adaptively tuned square-root cubature Kalman filter
PMSM
sensorless control
speed estimation
third degree spherical-radial cubature rule
position estimation
adaptively tuned SCKF
control algorithms
three-phase inverter-fed PMSM
numerical stability
nonlinear filter
general Bayesian estimation equation
multivariate moment integrals
CKF
improved noise rejection characteristics
permanent magnet synchronous motor drive
author_facet Gulur Raghavendra Gopinath
Prasad Das Shyama
author_sort Gulur Raghavendra Gopinath
title Sensorless control of PMSM using an adaptively tuned SCKF
title_short Sensorless control of PMSM using an adaptively tuned SCKF
title_full Sensorless control of PMSM using an adaptively tuned SCKF
title_fullStr Sensorless control of PMSM using an adaptively tuned SCKF
title_full_unstemmed Sensorless control of PMSM using an adaptively tuned SCKF
title_sort sensorless control of pmsm using an adaptively tuned sckf
publisher Wiley
series The Journal of Engineering
issn 2051-3305
publishDate 2019-04-01
description This study reports the application of an adaptively tuned square-root Cubature Kalman filter (SCKF) for the speed and position estimation of a permanent magnet synchronous motor (PMSM) drive. The proposed estimator is observed to exhibit improved noise rejection characteristics as compared to the hitherto widely applied extended Kalman filter (EKF) observer. A third degree spherical–radial cubature rule is used in the Cubature Kalman filter (CKF) to numerically compute the multivariate moment integrals of the general Bayesian estimation equation. CKF is a non-linear filter which avoids linearisation and the associated errors. The realisation of CKF using the square-root algorithm results in numerical stability, as with the realisation of EKF using the square-root algorithm. Simulation results are presented for a three-phase inverter-fed PMSM, along with the experimental results. The estimator and the control algorithms are realised on the MATLAB real-time environment, interfaced with the hardware using the National Instruments data acquisition system NI PCI-6221.
topic numerical stability
synchronous motor drives
permanent magnet motors
Kalman filters
nonlinear filters
Bayes methods
sensorless machine control
invertors
data acquisition
NI PCI-6221
National Instruments data acquisition system
square-root algorithm
adaptively tuned square-root cubature Kalman filter
PMSM
sensorless control
speed estimation
third degree spherical-radial cubature rule
position estimation
adaptively tuned SCKF
control algorithms
three-phase inverter-fed PMSM
numerical stability
nonlinear filter
general Bayesian estimation equation
multivariate moment integrals
CKF
improved noise rejection characteristics
permanent magnet synchronous motor drive
url https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8081
work_keys_str_mv AT gulurraghavendragopinath sensorlesscontrolofpmsmusinganadaptivelytunedsckf
AT prasaddasshyama sensorlesscontrolofpmsmusinganadaptivelytunedsckf
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