Nonlinear autoregressive movingaverage (NARMA-L2) controller for advanced AC motor control

A. Awwad, Haitham Abu-Rub, H. A. Toliyat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In this paper, speed controllers based on Artificial Neural Networks for vector control of AC motors are used. Tracking of the rotor speed is realized by adjusting the new weights of the network depending on the difference between the actual speed and the commanded speed. The controller is adaptive and is based on a nonlinear autoregressive moving average (NARMA-L2) algorithm. A comparative study between the proposed controllers and the conventional PI one will be presented and the advantages of the proposed solution over the conventional one will be shown. The rotor speed tracks the commanded one smoothly and rapidly, without overshoot and with very negligible steady state error. Computer simulation results are carried out to prove the claims.

Original languageEnglish
Title of host publicationProceedings - 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008
PublisherIEEE Computer Society
Pages1287-1292
Number of pages6
ISBN (Print)9781424417667
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008 - Orlando, FL, United States
Duration: 10 Nov 200813 Nov 2008

Other

Other34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008
CountryUnited States
CityOrlando, FL
Period10/11/0813/11/08

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Keywords

  • Artificial Neural Networks
  • Induction motor
  • Vector control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Awwad, A., Abu-Rub, H., & Toliyat, H. A. (2008). Nonlinear autoregressive movingaverage (NARMA-L2) controller for advanced AC motor control. In Proceedings - 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008 (pp. 1287-1292). [4758140] IEEE Computer Society. https://doi.org/10.1109/IECON.2008.4758140