Optimization of FOPID controller with hybrid Particle Swarm and Grey Wolf optimization for AVR System

Stephen Oladipo, Yanxia Sun, Zenghui Wang

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

10 Citations (Scopus)

Abstract

This paper presents the optimization of the fractional order proportional-integral-derivative (FOPID) controller with the hybrid particle swarm and grey wolf optimization (HPSGWO) algorithm in order to control the Automatic Voltage Regulator (AVR) system. To overcome the problem of premature convergence and enhance the efficiency of the proposed HPSGWO algorithm, an improved inertia weight is proposed. The response of the proposed HPSGWO is compared with the PSO and GWO algorithms. An integral-based fitness function namely Integral Time Absolute Error (ITAE) is selected as the fitness function while the rise time, settling time and peak overshoot are considered as the dynamic response analysis.

Original languageEnglish
Title of host publicationProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
EditorsGeetam Tomar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages273-279
Number of pages7
ISBN (Electronic)9781728193939
DOIs
Publication statusPublished - 25 Sept 2020
Event12th International Conference on Computational Intelligence and Communication Networks, CICN 2020 - Bhimtal, India
Duration: 25 Sept 202026 Sept 2020

Publication series

NameProceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020

Conference

Conference12th International Conference on Computational Intelligence and Communication Networks, CICN 2020
Country/TerritoryIndia
CityBhimtal
Period25/09/2026/09/20

Keywords

  • Automatic Voltage Regulator (AVR) optimization
  • Integral Time Absolute Error (ITAE)
  • Proportional Integral Derivative (PID) controller

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing

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