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Implementation of PID controller tuning using differential evolution and genetic algorithms

This paper presents the implementation of PID controller tuning using two modern heuristic techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The per...

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Main Authors: Saad, Mohd. Sazli, Jamaluddin, Hishamuddin, Mat Darus, Intan Zaurah
Format: Article
Published: ICIC International 2012
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/47082/
http://www.ijicic.org/ijicic-11-07073.pdf
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spelling my.utm.470822019-03-05T02:03:14Z http://eprints.utm.my/id/eprint/47082/ Implementation of PID controller tuning using differential evolution and genetic algorithms Saad, Mohd. Sazli Jamaluddin, Hishamuddin Mat Darus, Intan Zaurah TJ Mechanical engineering and machinery This paper presents the implementation of PID controller tuning using two modern heuristic techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The performance of these techniques is evaluated by setting their objective functions as mean square error (MSE) and integral absolute error (IAE). The reliability between DE and GA in consistently maintaining minimum MSE is studied. The performance of the PID control systems tuned using GA and DE methods are also compared with Ziegler-Nichols method. ICIC International 2012 Article PeerReviewed Saad, Mohd. Sazli and Jamaluddin, Hishamuddin and Mat Darus, Intan Zaurah (2012) Implementation of PID controller tuning using differential evolution and genetic algorithms. International Journal of Innovative Computing, Information and Control, 8 (11). pp. 7761-7779. ISSN 1349-4198 http://www.ijicic.org/ijicic-11-07073.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Saad, Mohd. Sazli
Jamaluddin, Hishamuddin
Mat Darus, Intan Zaurah
Implementation of PID controller tuning using differential evolution and genetic algorithms
description This paper presents the implementation of PID controller tuning using two modern heuristic techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The performance of these techniques is evaluated by setting their objective functions as mean square error (MSE) and integral absolute error (IAE). The reliability between DE and GA in consistently maintaining minimum MSE is studied. The performance of the PID control systems tuned using GA and DE methods are also compared with Ziegler-Nichols method.
format Article
author Saad, Mohd. Sazli
Jamaluddin, Hishamuddin
Mat Darus, Intan Zaurah
author_facet Saad, Mohd. Sazli
Jamaluddin, Hishamuddin
Mat Darus, Intan Zaurah
author_sort Saad, Mohd. Sazli
title Implementation of PID controller tuning using differential evolution and genetic algorithms
title_short Implementation of PID controller tuning using differential evolution and genetic algorithms
title_full Implementation of PID controller tuning using differential evolution and genetic algorithms
title_fullStr Implementation of PID controller tuning using differential evolution and genetic algorithms
title_full_unstemmed Implementation of PID controller tuning using differential evolution and genetic algorithms
title_sort implementation of pid controller tuning using differential evolution and genetic algorithms
publisher ICIC International
publishDate 2012
url http://eprints.utm.my/id/eprint/47082/
http://www.ijicic.org/ijicic-11-07073.pdf
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score 13.160551

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