The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents the comparisons of optimized extended Kalman filters (EKFs) using different fitness functions for speed-sensorless vector control of induction motors (IMs). In order to achieve high performance estimations of states/parameter by EKF algorithm, state and noise covariance matrices must be accurately selected. For this aim, instead of using time-consuming trial-and-error method to determine those covariance matrices, in this paper EKF algorithm is optimized by differential evolution algorithm (DEA) and multi-objective DEA (MODEA) with the utilization of different fitness functions. The optimally obtained set of each covariance matrices is used in EKF algorithm built on the same IM model and thus, the estimation results of the optimized EKF algorithms are compared in real-time experiments in order to conclude which fitness function is better for motion control applications.
Açıklama
Anahtar Kelimeler
Differential evolution algorithm (DEA), extended Kalman filter (EKF), induction motor (IM), multiobjective optimization, speed-sensorless control
Kaynak
Ieee Transactions on Industrial Electronics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
64
Sayı
6
