Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/892
Title: Speed estimation of an induction motor drive using an optimized extended Kalman filter
Authors: Shi, K
Chan, TF
Wong, YK
Ho, SL 
Keywords: Genetic algorithm
Induction motor
Kalman filter
Issue Date: Feb-2002
Publisher: IEEE
Source: IEEE transactions on industrial electronics, Feb. 2002, v. 49, no. 1, p. 124-133 How to cite?
Journal: IEEE transactions on industrial electronics 
Abstract: This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF.
URI: http://hdl.handle.net/10397/892
ISSN: 0278-0046
DOI: 10.1109/41.982256
Rights: © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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