Experimental parameter identification of nonlinear mechanical systems via meta-heuristic optimisation methods

Martinelli, C., Coraddu, A. and Cammarano, A. (2023) Experimental parameter identification of nonlinear mechanical systems via meta-heuristic optimisation methods. In: Brake, M. R.W., Renson, L., Kuether, R. J. and Tiso, P. (eds.) Nonlinear Structures and Systems: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023. Series: Conference Proceedings of the Society for Experimental Mechanics Series, 1. Springer, pp. 215-223. ISBN 9783031369988 (doi: 10.1007/978-3-031-36999-5_28)

Abstract

Meta-heuristic optimisation algorithms are high-level procedures designed to discover near-optimal solutions to optimisation problems. These strategies can efficiently explore the design space of the problems; therefore, they perform well even when incomplete and scarce information is available. Such characteristics make them the ideal approach for solving nonlinear parameter identification problems from experimental data. Nonetheless, selecting the meta-heuristic optimisation algorithm remains a challenging task that can dramatically affect the required time, accuracy, and computational burden to solve such identification problems. To this end, we propose investigating how different meta-heuristic optimisation algorithms can influence the identification process of nonlinear parameters in mechanical systems. Two mature meta-heuristic optimisation methods, i.e. particle swarm optimisation (PSO) method and genetic algorithm (GA), are used to identify the nonlinear parameters of an experimental two-degrees-of-freedom system with cubic stiffness. These naturally inspired algorithms are based on the definition of an initial population: this advantageously increases the chances of identifying the global minimum of the optimisation problem as the design space is searched simultaneously in multiple locations. The results show that the PSO method drastically increases the accuracy and robustness of the solution, but it requires a quite expensive computational burden. On the contrary, the GA requires similar computational effort but does not provide accurate solutions.

• Item Type: Book Sections
• Additional Information: eISBN: 9783031369995. The authors would like to acknowledge the Institution of Engineering and Technology (IET) and the following NERC and EPSRC grants: GALLANT,Glasgow as a Living Lab Accelerating Novel Transformation (No. NE/W005042/1), RELIANT, Risk EvaLuatIon fAst iNtelligent Tool for COVID19 (No. EP/V036777/1).
• Keywords: Experimental nonlinear analysis, nonlinear dynamics, parameter identification, meta-heuristic optimisation, nonlinear frequency response.
• Status: Published
• Glasgow Author(s) Enlighten ID: Martinelli, Mr Cristiano and Cammarano, Dr Andrea
• Authors: Martinelli, C., Coraddu, A., and Cammarano, A.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Publisher: Springer
• ISBN: 9783031369988
• Copyright Holders: Copyright © 2024 The Society for Experimental Mechanics, Inc.
• First Published: First published in Nonlinear Structures & Systems, Volume 1, 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
313196		NERC Strategic Programme Call	Jaime Toney	Natural Environment Research Council (NERC)	NE/W005042/1	GES - Geography
311655		Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19	Andrea Cammarano	Engineering and Physical Sciences Research Council (EPSRC)	EP/V036777/1	ENG - Autonomous Systems & Connectivity