Neuro-control approach of switched reluctance motor drives

V. Trifa, Elena Gaura, L Moldovan

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

The purpose of the paper is to present several studies on neural networks used for the modelling of a switched reluctance motor (SRM) with variable structure control. A positioning system using a four-phase SRM is presented, in which the position error is processed by a sliding-mode controller. The control unit represents the subject of a neural network-based model. The proposed network system has a feedforward type architecture, structured on three layers of processing units. The networks are trained using the BKP algorithm. Once the network system is trained, it is integrated as a part of the positioning system. The training and testing sets of examples are obtained by numerical simulation of the positioning system using the Matlab environment.
Original languageEnglish
Title of host publicationProceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96)
EditorsMarco DeSario, Bruno Maione, Pasquale Pugliese, Mario Savino
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1461-1464
Number of pages4
ISBN (Print)0780331095
DOIs
Publication statusPublished - 1996
Externally publishedYes
Event8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (Melecon 96) - BARI, Italy
Duration: 13 May 199616 May 1996

Publication series

NameIEEE Mediterranean Electrotechnical Conference-MELECON
PublisherIEEE

Conference

Conference8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (Melecon 96)
Country/TerritoryItaly
CityBARI
Period13/05/9616/05/96

Fingerprint

Dive into the research topics of 'Neuro-control approach of switched reluctance motor drives'. Together they form a unique fingerprint.

Cite this