NDLI: Robust control of a field oriented linear induction motor drive

Content Provider	IEEE Xplore Digital Library
Author	Hassan, A.A. Mohamed, Y.S. Mohamed, T.H.
Copyright Year	2006
Description	Author affiliation: Electr. Eng. Dept., El-Minia Univ., El-Minia, Egypt (Hassan, A.A.; Mohamed, Y.S.; Mohamed, T.H.)
Abstract	This paper presents a robust state feedback controller for a linear induction motor drive without a seed sensor. This is accomplished via employing the linear quadratic Gaussian approach. The standard Kalman filter technique has been employed to estimate the full states of the system including the motor speed. The primary current components are only the measured signals. The computational burden could be minimized to a great extent by calculating the optimal state feedback gains and Kalman state space model off-line. The proposed controller has the advantages of robustness, easy implementation and adequate performance in face of uncertainties. Furthermore, higher reliability is gained by excluding the speed encoder. Digital simulations have been carried out in order to evaluate the effectiveness of the proposed scheme. The linear induction motor with the roosed controller has been tested through speed command variation, mismatched parameters and load disturbance. The results prove that the linear induction motor with the proposed controller has good robustness in face of uncertainties. Moreover, accurate tracking performance has been achieved.
Starting Page	41
Ending Page	47
File Size	1785072
Page Count	7
File Format	PDF
ISBN	9781424451111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-19
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	El-Minia University
Subject Keyword	Robust control Induction motor drives State feedback Induction motors Current measurement Linear feedback control systems Digital simulation State-space methods Kalman filters Linear induction motor - Linear quadratic Gaussian robust control State estimation
Content Type	Text
Resource Type	Article

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