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Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.

This paper presents the dynamic modeling of the gradient flexible plate system using System Identification method based on autoregressive with exogenous input model structure and estimated by Grey Wolf Optimization. The experimental rig of the gradient flexible plate was integrated with the data acq...

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Main Authors: Hassan, M. H., Jamali, A., R., Lidyana, Suffian, M. S. Z. M., Hadi, M. S., Mat Darus, I. Z.
Format: Article
Language:English
Published: Tamkang University 2023
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/106277/1/IZMatDarus2023_GreyWolfOptimizationforIntelligentParametricModeling.pdf
http://eprints.utm.my/106277/
http://dx.doi.org/10.6180/jase.202309_26(9).0001
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spelling my.utm.1062772024-06-20T06:06:53Z http://eprints.utm.my/106277/ Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure. Hassan, M. H. Jamali, A. R., Lidyana Suffian, M. S. Z. M. Hadi, M. S. Mat Darus, I. Z. TJ Mechanical engineering and machinery This paper presents the dynamic modeling of the gradient flexible plate system using System Identification method based on autoregressive with exogenous input model structure and estimated by Grey Wolf Optimization. The experimental rig of the gradient flexible plate was integrated with the data acquisition and instrumentation to obtain input-output vibration data. The performances of developed models were validated through one step ahead prediction, mean squared error, and correlation tests. The model was verified using the pole-zero diagram to confirm its stability for the controller development. Results indicated that the optimum model to represent the dynamic system of gradient flexible plate was achieved by model order 4 with the mean squared error of 8.0496×10-6. The correlation results proved that the model was unbiased, and falls within the 95% confidence level. Likewise, the model was found to be stable as all the poles of transfer function were within the unit circle. Therefore, the identified model can be confidently used for the controller development to suppress undesirable vibration in the gradient flexible plate structure. Tamkang University 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/106277/1/IZMatDarus2023_GreyWolfOptimizationforIntelligentParametricModeling.pdf Hassan, M. H. and Jamali, A. and R., Lidyana and Suffian, M. S. Z. M. and Hadi, M. S. and Mat Darus, I. Z. (2023) Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure. Journal of Applied Science and Engineering (Taiwan), 26 (9). pp. 1207-1214. ISSN 2708-9967 http://dx.doi.org/10.6180/jase.202309_26(9).0001 DOI: 10.6180/jase.202309_26(9).0001
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/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Hassan, M. H.
Jamali, A.
R., Lidyana
Suffian, M. S. Z. M.
Hadi, M. S.
Mat Darus, I. Z.
Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
description This paper presents the dynamic modeling of the gradient flexible plate system using System Identification method based on autoregressive with exogenous input model structure and estimated by Grey Wolf Optimization. The experimental rig of the gradient flexible plate was integrated with the data acquisition and instrumentation to obtain input-output vibration data. The performances of developed models were validated through one step ahead prediction, mean squared error, and correlation tests. The model was verified using the pole-zero diagram to confirm its stability for the controller development. Results indicated that the optimum model to represent the dynamic system of gradient flexible plate was achieved by model order 4 with the mean squared error of 8.0496×10-6. The correlation results proved that the model was unbiased, and falls within the 95% confidence level. Likewise, the model was found to be stable as all the poles of transfer function were within the unit circle. Therefore, the identified model can be confidently used for the controller development to suppress undesirable vibration in the gradient flexible plate structure.
format Article
author Hassan, M. H.
Jamali, A.
R., Lidyana
Suffian, M. S. Z. M.
Hadi, M. S.
Mat Darus, I. Z.
author_facet Hassan, M. H.
Jamali, A.
R., Lidyana
Suffian, M. S. Z. M.
Hadi, M. S.
Mat Darus, I. Z.
author_sort Hassan, M. H.
title Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
title_short Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
title_full Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
title_fullStr Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
title_full_unstemmed Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
title_sort grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.
publisher Tamkang University
publishDate 2023
url http://eprints.utm.my/106277/1/IZMatDarus2023_GreyWolfOptimizationforIntelligentParametricModeling.pdf
http://eprints.utm.my/106277/
http://dx.doi.org/10.6180/jase.202309_26(9).0001
_version_ 1802977255361609728
score 13.211869

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