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A Comparative Evaluation of Metaheuristic Optimization Methods for Control Applications

  • Universidad San Francisco de Quito

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

2 Scopus citations

Abstract

This paper presents a comparative study of metaheuristic techniques for optimizing tuning in two controllers applied to processes characterized by long-dead times. Experimental validation was conducted on an Arduino Temperature Control Lab with additional software-induced delays. The investigation involved tuning the Smith Predictor and PI controllers by utilizing three distinct meta-heuristic optimization algorithms: the Whale Optimization Algorithm, Gray Wolf Optimizer, and Ant Lion Optimizer. The pursuit was guided by the minimization of the Integral Square Error, serving as the cost function. The effectiveness of these control strategies was evaluated using diverse performance indices. The results accentuate the predominance of the Smith Predictor coupled with the Whale Optimization Algorithm, emerging as the most suitable and balanced choice among the examined control methodologies.

Original languageEnglish
Title of host publicationECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting
EditorsDavid Rivas Lalaleo, Manuel Ignacio Ayala Chauvin
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350338232
DOIs
StatePublished - 2023
Event7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023 - Ambato, Ecuador
Duration: 10 Oct 202313 Oct 2023

Publication series

NameECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting

Conference

Conference7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023
Country/TerritoryEcuador
CityAmbato
Period10/10/2313/10/23

Keywords

  • PI
  • Smith Predictor
  • TCLAB
  • dead-time
  • metaheuristic
  • optimization algorithms

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