Weighted parametric model identification of induction motors with variable loads using FNN structure and NN2TF algorithm

Tarek A. Tutunji, Ashraf Saleem

Research output: Contribution to journalArticle

1 Citation (Scopus)


Induction motors exhibit non-linear behaviour and are difficult to model. Furthermore, load disparities cause speed variations and therefore predicting the motor’s response is challenging. In this paper, feedforward neural networks (FNNs) are used to model induction motors at two load levels (i.e. no-load and full-load). The two FNN models are then transformed into auto-regressive moving-average (ARMA) models using a new NN2TF algorithm. A weighted parametric model is then formulated by combining both ARMA models to provide appropriate transfer functions at different loads ranging from no-load to full-load. In order to validate the developed model, experimental data (with voltage/speed as input/output) is collected from an induction motor plant at five different load levels and used to test the proposed model. Simulation results show that the estimated model produced dynamic responses that follow the experimental data with good accuracy, regardless of the load.

Original languageEnglish
Pages (from-to)1645-1658
Number of pages14
JournalTransactions of the Institute of Measurement and Control
Issue number5
Publication statusPublished - Mar 1 2018



  • ARMA models
  • induction motors
  • neural networks
  • robust design
  • system identification

ASJC Scopus subject areas

  • Instrumentation

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