Asynchronous induction motor speed control using takagi-sugeno fuzzy logic

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

4 Scopus citations

Abstract

Induction motors are widely used electrical machine in different kind of industry due to its simplicity, ruggedness, versatility, reliability, low cost, quiet operation, less maintenance, high performance and longevity. This paper presents Takagi-Sugeno (T-S) fuzzy logic based speed control of an induction motor. Constant voltage and frequency ratio-based control or scaler control is used to maintain its speed at desired value under load change. Since fuzzy logic includes human like idea in its algorithm, it can smoothly control a range of operating point of the induction motor whereas conventional PI control gives poor performance over wide range of variation. This work also presents a comparison between conventional PI control and fuzzy logic control for induction motor (IM) drive considering wide variation of load. Matlab Simulink platform is used to design the Takagi-Sugeno (T-S) fuzzy model. Control properties of induction motor are derived from its d-q based mathematical model. The result from disturbance analysis under sudden load change shows superior dynamic performance of Fuzzy logic controller over conventional PI controller. It provides an alternative control to ensure high-performance industrial drive over the conventional PI controller.

Original languageEnglish
Title of host publicationICECE 2018 - 10th International Conference on Electrical and Computer Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages249-252
Number of pages4
ISBN (Electronic)9781538674826
DOIs
StatePublished - 2 Jul 2018

Publication series

NameICECE 2018 - 10th International Conference on Electrical and Computer Engineering

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Scaler control and PI control etc
  • Takagi-Sugeno Fuzzy logic

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation

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