Sensorless control of PMSM using an adaptively tuned SCKF
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 hi...
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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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1724165179288059904 |