NDLI: MRAS speed observer for high performance linear induction motor drives based on linear neural networks

Content Provider	IEEE Xplore Digital Library
Author	Accetta, A. Cirrincione, M. Pucci, M. Vitale, G.
Copyright Year	2011
Description	Author affiliation: University of Palermo, Italy (Accetta, A.) \|\| ISSIA-CNR (Insitute of Intelligent Systems for the Automation), Palermo, Italy (Pucci, M.; Vitale, G.) \|\| Université Technologique de Belfort Montbeliard, (UTBM), France (Cirrincione, M.)
Abstract	This paper proposes a Neural Network (NN) MRAS (Model Reference Adaptive System) speed observer suited for linear induction motor (LIM) drives. The voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been obtained. Then, equations of the induced part have been discretized and rearranged so as to be represented by a linear neural network the TLS EXIN neuron, which has been used to compute the machine linear speed on-line and in recursive form. The proposed NN MRAS observer has been tested experimentally on a suitably developed test setup. Its performance has been also compared to the classic MRAS speed observer.
Starting Page	1765
Ending Page	1772
File Size	964210
Page Count	8
File Format	PDF
ISBN	9781457705427
e-ISBN	9781457705410
DOI	10.1109/ECCE.2011.6063997
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Field Oriented Control (FOC) Linear Induction Motor (LIM) Neural Networks (NN) Model Reference Adaptive Systems (MRAS) Adaptive filters Artificial neural networks Sensorless control Observers Mathematical model Inductors Equations
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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