NDLI: Recursive Identification of Electric Drives using Gray-Box Models

Content Provider	IEEE Xplore Digital Library
Author	Aquino-Lugo, Angel Velez-Reyes, Miguel
Copyright Year	2006
Description	Author affiliation: Senior Member, Center for Power Electronic Systems, University of Puerto Rico-Mayagüez, P.O. Box 9048, Mayagüez, PR 00681 USA. E-mail: m.velez@ieee.org (Velez-Reyes, Miguel) \|\| Center for Power Electronic Systems, University of Puerto Rico-Mayagüez, P.O. Box 9048, Mayagüez, PR 00681 USA. E-mail: angel.aquino@ieee.org (Aquino-Lugo, Angel)
Abstract	Universal electric motor drives should have the capability to tune the drive control system to drive different loads and maintain system performance for a wide range of loads. Gray-box modeling using neural networks is presented here as a possible solution for the identification of electric motor drive systems. In the proposed gray-box modeling, the drive system is divided into the known part governed by the physical laws, which in our case is the electrical subsystem, and an unknown part, which in our case is the mechanical subsystem. The mechanical part is modeled with a black box model using a radial basis neural network. Linear regression models are proposed for the mechanical and electrical subsystems and used to design a two-stage parameter estimation algorithm based on linear least squares. Simulation and experimental results are presented showing the potential of the approach.
Starting Page	640
Ending Page	644
File Size	2347542
Page Count	5
File Format	PDF
ISBN	1424401720
ISSN	15483746
DOI	10.1109/MWSCAS.2006.382144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-06
Publisher Place	Puerto Rico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system modeling Neural networks Automatic control Control systems Motor drives Electric motors Linear regression Least squares approximation Automation Senior members
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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