Identification and control of induction machines using artificial neural networks

Wishart, Michael T. & Harley, Ronald G. (1995) Identification and control of induction machines using artificial neural networks. IEEE Transactions on Industry Applications, 31(3), pp. 612-619.

View at publisher


This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Impact and interest:

88 citations in Scopus
62 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

56 since deposited on 13 Oct 2010
0 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 37934
Item Type: Journal Article
Refereed: Yes
Keywords: adaptive control, asynchronous machines, control system analysis, control system synthesis, electric current control, machine control, machine theory, neurocontrollers, parameter estimation, rotors, velocity control, artificial neural networks, dynamics identification, induction machines, performance, rotor speed, stator currents, vector control scheme
DOI: 10.1109/28.382123
ISSN: 0093-9994
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Industrial Electronics (090603)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 1995 IEEE
Copyright Statement: 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.
Deposited On: 13 Oct 2010 21:58
Last Modified: 10 Aug 2011 14:31

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page