Home / Regular Issue / JST Vol. 31 (2) Mar. 2023 / JST-3521-2022


A Single Objective Crow Search for Modelling of Horizontal Flexible Plate Structure

Aida Nur Syafiqah Shaari, Muhamad Sukri Hadi and Abdul Malek Abdul Wahab

Pertanika Journal of Science & Technology, Volume 31, Issue 2, March 2023

DOI: https://doi.org/10.47836/pjst.31.2.21

Keywords: Active vibration control, crow search, flexible structure, metaheuristic, modelling, swarm intelligence algorithm, system identification

Published on: 20 March 2023

The magnificent features of flexible plate structure, including lightweight and high-speed response, resulted in additional market demand, especially in the automotive and manufacturing industries. Nevertheless, the structure may incur structural damage and performance degradation when the system encounters excessive vibration. Therefore, a system identification approach utilising a metaheuristic algorithm via crow search to develop a horizontal flexible plate (HFP) model for vibration control is introduced in this paper. Crow search (CS) is a modern algorithm inspired by a crow’s intellectual operation to store additional food and memorise the food storage location. In this study, CS is employed to optimise the objective function, which is the mean squared error for accomplishing a precise predicted model in replicating the dynamic response of the actual structure. Hence, the preliminary action for modelling using this approach is designing and fabricating an HFP rig for experimentally gathering the real input-output vibration data. After that, the mathematical modelling utilising the CS algorithm was implemented using a parametric model structure. Finally, the best-fit model is chosen for the representation of the HFP based on the lowest mean squared error, correlation test within a 95% confidence level and stability in a pole-zero plot. The simulation result reveals that the CS algorithm with a second-order estimated model accomplished a minimum MSE of 1.1168 × 10-5, an unbiased correlation test and excellent stability for the HFP structure.

  • Adhi, A., Santosa, B., & Siswanto, N. (2018). A meta-heuristic method for solving scheduling problem: Crow search algorithm. In Materials Science and Engineering (pp. 1-6). IOP Publishing. https://doi.org/10.1088/1757-899X/337/1/012003

  • Agarwal, H., & Agarwal, R. (2017). First industrial revolution and second industrial revolution: Technological differences and the differences in banking and financing of the firms. Saudi Journal of Humanities and Social Sciences, 5(4), 1062-1066.

  • Aly, W. M. (2013). Evaluation of cuckoo search usage for model parameters estimation. International Journal of Computer Applications, 78(11), 1-6.

  • Askarzadeh, A. (2016). A novel metaheuristsic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169, 1-12. https://doi.org/10.1016/j.compstruc.2016.03.001

  • Eek, R. T. P., Darus, I. Z. M., Sahlan, S., Samin, P. M., & Shaharuddin, N. M. (2016). Implementation of swarm algorithm in modeling a flexible beam structure. Journal of Vibroengineering, 18(8), 4914-4934. https://doi.org/10.21595/jve.2015.15182

  • Erkoc, M. E., & Karaboga, N. (2021). Sparse signal reconstruction by swarm intelligence algorithms. Engineering Science and Technology-An International Journal-JESTECH, 24(2), 319-330. https://doi.org/10.1016/j.jestch.2020.09.006

  • Gheisarnejad, M. (2018). An effective hybrid harmony search and cuckoo optimization algorithm based fuzzy PID controller for load frequency control. Applied Soft Computing, 24(3), 121-138. https://doi.org/10.1016/j.asoc.2018.01.007

  • Hadi, M. S., Darus, I. Z. M., & Yatim, H. M. (2013). Modeling flexible plate structure system with free-free-clamped-clamped (FFCC) edges using particle swarm optimization. In 2013 IEEE Symposium on Computers & Informatics (pp. 39-44). IEEE Publishing. https://doi.org/10.1109/ISCI.2013.6612372

  • Hadi, M. S., Darus, I. Z. M., Eek R. T. P., & Yatim, H. M. (2014). Swarm intelligence for modeling a flexible plate structure system with clamped-clamped-free-free boundary condition edges. In 2014 IEE Symposium on Industrial Electronics & Applications (ISIEA) (pp. 119-124). IEEE Publishing. https://doi.org/10.1109/ISIEA.2014.8049883

  • Hadi, M. S., Hashim, M. H., & Darus, I. Z. M. (2012). Genetic modeling of a rectangular flexible plate system with free-free-clamped-clamped (FFCC) edges. In 2012 IEEE Conference on Control, Systems and Industrial Informatics (ICCSII) (pp. 173-179). IEEE Publishing. https://doi.org/10.1109/CCSII.2012.6470496

  • Hou, X. (2018). A variable structural control for a flexible plate. American Review of Mathematics and Statistics, 6(2), 1-8. https://doi.org/10.15640/arms.v6n2a1

  • Hussien, A. G., Amin, M., Wang, M., Liang, G., Alsanad, A., Gumaei, A., & Chen, H. (2020). Crow search algorithm: Theory, recent advances, and applications. IEEE Access, 8, 173548-173565. https://doi.org/10.1109/ACCESS.2020.3024108

  • Islam, J., Vasant, P., Negash, B. M., Gupta, A., Watada, J., & Banik, A. (2020). Well placement of optimization using firefly algorithm and crow search algorithm. Journal of Advanced Engineering and Computation, 4(3), 181-195. http://dx.doi.org/10.25073/jaec.202043.287

  • Jiang, H., Liu, T., He, P. H., Ding, Y. H., & Chen, Q. S. (2021). Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features. Food Chemistry, 338, Article 127828. https://doi.org/10.1016/j.foodchem.2020.127828

  • Khairuddin, I. M., Dahalan, A. S., Abidin, A. F. Z., Lai, Y. Y., Nordin, N. A., Sulaiman, S. F., & Amer, N. H. (2014). Modeling and simulation of swarm intelligence algorithms for parameters tuning of PID controller in industrial couple tank system. Advanced Materials Research, 903, 321-326. https://doi.org/10.4028/www.scientific.net/AMR.903.321

  • Kivi, M. E., & Majidnezhad, V. (2021). A novel swarm intelligence algorithm inspired by the grazing of sheep. Journal of Ambient Intelligence and Humanized Computing, 13, 1201-1213. https://doi.org/10.1007/s12652-020-02809-y

  • Majhi, S. K., Sahoo, M., & Pradhan, R. (2020). A space transformational crow search algorithm for optimization problems. Evolutionary Intelligence, 13(3), 345-364.

  • Mamuda, M., & Mukhtar, M. (2017). Formulation of renewable energy lubricants using antimony dialkyl dithio-carbonate and zinc dialkyl dithio-phosphate additives. Nigerian Journal of Renewable Energy, 17(1&2), 55-64.

  • Matin, F., Cheraghi, H., Sobhani, N., Piltan, F., & Rahmani, M. (2016). Research on PID-based minimum rule base fuzzy controller in active joint dental automation. International Journal of Grid and Distributed Computing, 9(6), 315-338. http://dx.doi.org/10.14257/ijgdc.2016.9.6.29

  • Ministry of Human Resource. (2021). Fatal accident case. Department of Occupational Safety and Health. https://www.dosh.gov.my/index.php/fatal-accident-case

  • Mohajan, H. K. (2019). The first industrial revolution: Creation of a new global human era. Journal of Social Sciences and Humanities, 5(4), 377-387.

  • Mohammed, M. J., Ahmed, M. K., & Abbas, B. A. (2019). Modeling and control of horizontal flexible plate using PID-CS controller. Journal of Mechanical Engineering Research and Developments (JMERD), 24(4), 138-142. http://dx.doi.org/10.26480/jmerd.04.2019.138.142

  • Nagendramma, P., & Kaul, S. (2012). Development of ecofriendly/biodegradable lubricants: An overview. Renewable and Sustainable Energy Reviews, 16(1), 764-774. https://doi.org/10.1016/j.rser.2011.09.002

  • Nayak, A., & Singh, M. (2015). Study of tuning of PID controller by using particle swarm optimization. International Journal of Advanced Engineering Research and Studies, 2015, 346-350.

  • Okiy, S., Okeye, C. C. N., & Igboanugo, A. C. (2015). Transfer function modelling: A literature survey. Research Journal of Applied Sciences, Engineering and Technology, 11(11), 1265-1279.

  • Pedro J. O., & Tshabalala T. (2015). Hybrid NNMPC/PID control of a two-link flexible manipulator with actuator dynamics. In 10th Asian Control Conference (ASCC) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/ascc.2015.7244737

  • Rao, V. S., George, V. I., Kamath, S., & Shreesha, C. (2016). Performance evaluation of reliable H infinity observer controller with robust PID controller designed for TRMS with sensor, actuator failure. Far East with sensor, actuator failure. Far East Journal of Electronics and Communications, 16(2), 355-380. http://dx.doi.org/10.17654/EC016020355

  • Souza, R. C., Coelho, L. D., Macedo, C. A., & Pierezan, J. (2018). A v-shaped binary crow search algorithm for feature selection. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE Publishing. https://doi.org/10.1109/CEC.2018.8477975

  • Tavakolpour, A., Mailah, M., & Darus, I. Z. M. (2011). Modeling and simulation of a novel active vibration control system for flexible structures. WSEAS Transactions on Systems and Control, 5(6),184-195.

  • Tsipianitis, A., & Tsompanakis, Y. (2021). Optimizing the seismic response of base-isolated liquid storage tanks using swarm intelligence algorithms. Computers & Structures, 243, Article 106407. https://doi.org/10.1016/j.compstruc.2020.106407

  • Visioli, A. (2012). Research trends for PID controller. ACTA Polytechnica, 52(5), 144-150.

  • Yatim, H. M., Darus, I. Z. M., & Hadi, M. S. (2013). Particle swarm optimization for identification of a flexible manipulator system. In IEEE Symposium on Computers & Informatics (pp. 112-117). IEEE Publishing. https://doi.org/10.1109/ISCI.2013.6612386