Artificial-neural-network-based sensorless nonlinear control of induction motors

Miroslaw Wlas, Zbigniew Krzemiński, Jarosław Guziński, Haitham Abu-Rub, Hamid A. Toliyat

Research output: Contribution to journalArticle

93 Citations (Scopus)


In this paper, two architectures of artificial neural networks (ANNs) are developed and used to correct the performance of sensorless nonlinear control of induction motor systems. Feed-forward multilayer perception, an Elman recurrent ANN, and a two-layer feedforward ANN is used in the control process. The method is based on the use of ANN to get an appropriate correction for improving the estimated speed. Simulation and experimental results were carried out for the proposed control system. An induction motor fed by voltage source inverter was used in the experimental system. A digital signal processor and field-programmable gate arrays were used to implement the control algorithm.

Original languageEnglish
Pages (from-to)520-528
Number of pages9
JournalIEEE Transactions on Energy Conversion
Issue number3
Publication statusPublished - Sep 2005
Externally publishedYes



  • Artificial neural networks (ANNs)
  • Digital signal processor (DSP)
  • Field-programmable gate arrays (FPGAs)
  • Induction motor
  • Nonlinear control
  • Observer system
  • Sensorless control

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology
  • Electrical and Electronic Engineering

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