Grey Wolf Optimization For Intelligent Parametric Modeling Of Gradient Flexible Plate Structure

M.H.  Hassan; A.  Jamali; M.S.Z.M  Suffian; M.S  Hadi; I.Z. Mat  Darus

doi:10.6180/jase.202309_26(9).0001

Grey Wolf Optimization For Intelligent Parametric Modeling Of Gradient Flexible Plate Structure

M.H. Hassan¹, A. JamaliThis email address is being protected from spambots. You need JavaScript enabled to view it.¹, R. Lidyana¹, M.S.Z.M Suffian¹, M.S Hadi², and I.Z. Mat Darus³

¹Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94000 Sarawak, Malaysia.
²School of Mechanical Engineering, College of Engineering, Universiti Teknologi MARA, 40450, Shah Alam, Selangor.
³School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru.

Received: August 28, 2022
Accepted: October 23, 2022
Publication Date: November 23, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202309_26(9).0001

ABSTRACT

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.

Keywords: Gradient flexible plate, grey wolf optimization, parametric modeling

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