State Estimation of Two-Phase Permanent Magnet Synchronous Motor

The goal of this paper is to estimate the states of two-phase permanent magnet synchronousmotor (PMSM). The system is highly nonlinear and one therefore cannot directly use any linearsystem tools for estimation. However, if one can linearize the system around a nominal(possibly time-varying) operati...

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Main Authors: Amjed J. Hamidi, Ahmed Alaa Ogla, Yaser Nabeel Ibrahem
Format: Article
Language:English
Published: Unviversity of Technology- Iraq 2009-05-01
Series:Engineering and Technology Journal
Subjects:
Online Access:https://etj.uotechnology.edu.iq/article_35695_e45098346e6c721da77a7c13caafb862.pdf
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author Amjed J. Hamidi
Ahmed Alaa Ogla
Yaser Nabeel Ibrahem
author_facet Amjed J. Hamidi
Ahmed Alaa Ogla
Yaser Nabeel Ibrahem
author_sort Amjed J. Hamidi
collection DOAJ
description The goal of this paper is to estimate the states of two-phase permanent magnet synchronousmotor (PMSM). The system is highly nonlinear and one therefore cannot directly use any linearsystem tools for estimation. However, if one can linearize the system around a nominal(possibly time-varying) operating point then linear system tools could be used for control andestimation. Firstly, the error covariance matrices of measurement and process would be derivedwhen the system inputs and outputs are subjected to uncertain variations. Then, the corruptednoisenonlinear model of the system will be discretized and extended to be suitable for applyingstandard discrete Kalman filter (KF) for state estimation purpose. The entire state estimatedsystem has been modeled using MATLAB/SIMULINK blocks. The state estimation algorithmand the motor discretized model are coded inside special S-functions of m-file type.
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spelling doaj.art-554e0cbe28d24941b888180c934e168b2024-02-04T17:49:21ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582009-05-012771435144310.30684/etj.27.7.1735695State Estimation of Two-Phase Permanent Magnet Synchronous MotorAmjed J. HamidiAhmed Alaa OglaYaser Nabeel IbrahemThe goal of this paper is to estimate the states of two-phase permanent magnet synchronousmotor (PMSM). The system is highly nonlinear and one therefore cannot directly use any linearsystem tools for estimation. However, if one can linearize the system around a nominal(possibly time-varying) operating point then linear system tools could be used for control andestimation. Firstly, the error covariance matrices of measurement and process would be derivedwhen the system inputs and outputs are subjected to uncertain variations. Then, the corruptednoisenonlinear model of the system will be discretized and extended to be suitable for applyingstandard discrete Kalman filter (KF) for state estimation purpose. The entire state estimatedsystem has been modeled using MATLAB/SIMULINK blocks. The state estimation algorithmand the motor discretized model are coded inside special S-functions of m-file type.https://etj.uotechnology.edu.iq/article_35695_e45098346e6c721da77a7c13caafb862.pdftwophase permanent magnet synchronous motorkalman filterextended kalman filtermodellingmatlabfunction
spellingShingle Amjed J. Hamidi
Ahmed Alaa Ogla
Yaser Nabeel Ibrahem
State Estimation of Two-Phase Permanent Magnet Synchronous Motor
Engineering and Technology Journal
two
phase permanent magnet synchronous motor
kalman filter
extended kalman filter
modelling
matlab
function
title State Estimation of Two-Phase Permanent Magnet Synchronous Motor
title_full State Estimation of Two-Phase Permanent Magnet Synchronous Motor
title_fullStr State Estimation of Two-Phase Permanent Magnet Synchronous Motor
title_full_unstemmed State Estimation of Two-Phase Permanent Magnet Synchronous Motor
title_short State Estimation of Two-Phase Permanent Magnet Synchronous Motor
title_sort state estimation of two phase permanent magnet synchronous motor
topic two
phase permanent magnet synchronous motor
kalman filter
extended kalman filter
modelling
matlab
function
url https://etj.uotechnology.edu.iq/article_35695_e45098346e6c721da77a7c13caafb862.pdf
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