By Topic

Square Root Unscented Kalman Filters for state estimation of induction motor drives

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jafarzadeh, S. ; Dept. of Electr. & Biomed. Eng., Univ. of Nevada, Reno, NV, USA ; Lascu, C. ; Fadali, M.S.

This paper investigates the application, design, and implementation of the Square Root Unscented Kalman Filter (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UT) in the prediction step in order to preserve the stochastic characteristics of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering and has the potential of reducing the errors in numerical computation. We discuss the theoretical aspects and implementation details of the SRUKF for IM drives. Experimental results for a direct torque controlled drive are presented for a wide speed range of operation, with focus on low speed performance. A comparison with the conventional EKF is included. It is concluded that the SRUKF is a viable and powerful tool for IM state estimation.

Published in:

Energy Conversion Congress and Exposition (ECCE), 2011 IEEE

Date of Conference:

17-22 Sept. 2011