Fuzzy-like adaptive position control of induction motor

M. Bilal Qureshi*, Laiq Khan, Shahid Qamar, Muhammad Shafiq

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

A novel technique named Fuzzy-Like Adaptive Control (FLAC) is proposed for adaptive position control of a nonlinear dynamic plant such as the Induction Motor (IM). To accurately track the rotor position of an IM, the key has been a proper design of a controller. The proposed method utilizes the Modified Gaussian Radial Basis Function Neural Network (M-GRBFNN) for the learning of error. This scheme involves edge triggered standard deviation to update the centers and variances of the error. The major benefit of the proposed approach is ease along with excellent performance. It has been proved that the tracking error converges to the neighborhood of zero. The success of the proposed scheme is confirmed with the help of real-time experiments.

Original languageEnglish
Title of host publication2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016
EditorsTarek Sobh, Khaled Elleithy
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509007998
DOIs
Publication statusPublished - Mar 1 2017
Event2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016 - Bridgeport, United States
Duration: Oct 14 2016Oct 15 2016

Publication series

Name2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016

Other

Other2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016
Country/TerritoryUnited States
CityBridgeport
Period10/14/1610/15/16

Keywords

  • Adaptive tracking
  • Gaussian radial basis function
  • Induction motor
  • Position control
  • neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

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