An improved multi-objective fuzzy decision based predictive torque control of induction motor drive

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

2 Scopus citations

Abstract

Predictive torque control (PTC) gains a lot of interest lately. Although the flux-weighting factor is the only tuning parameter in PTC, its selection is not a trivial task. This paper proposes an improved multi-objective fuzzy decision based method to select the best compromise voltage vector. The salient feature of the proposed approach lies in its capability to avoid high torque ripple and the need of extra priority factors, which increase the complexity of the technique. In the proposed approach, the membership functions are normalized using the global optimal values. As a result, an efficient compromise solution between torque and flux ripples minimization is automatically achieved. An experimental set up is used to validate the proposed method. Torque ripple, flux ripple, current total harmonic distortion, and average frequency are used as criteria for performance comparison to a number of reported methods in literature. The results show considerable improvement in torque ripple with slightly increased flux ripple proving the simplicity and compromising ability of the proposed approach.

Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8675-8680
Number of pages6
ISBN (Electronic)9781538611272
DOIs
StatePublished - 15 Dec 2017

Publication series

NameProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Volume2017-January

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Induction motor
  • fuzzy decision
  • model predictive torque control
  • multi-objective
  • weighting Factor

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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