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Optimized Kalman filters for sensorless vector control induction motor drives
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<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions.</span>

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Publication Date
Fri Mar 01 2019
Journal Name
Applied Acoustics
Theoretical model of absorption coefficient of an inhomogeneous MPP absorber with multi-cavity depths
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Publication Date
Mon Sep 22 2025
Journal Name
Journal Of Studies And Researches Of Sport Education
The effect of using the educational bag on the level of learning some offensive skills with the epee weapon
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Publication Date
Sat May 31 2025
Journal Name
3rd International Scientific Conference For Human And Social Studies And Epistemological Challenges
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