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

Cristiano Martinelli*, Andrea Coraddu, Andrea Cammarano

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationNonlinear Structures and Systems, Volume 1 - Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023
EditorsMatthew R.W. Brake, Ludovic Renson, Robert J. Kuether, Paolo Tiso
PublisherSpringer
Pages215-223
Number of pages9
ISBN (Electronic)978-3-031-36999-5
ISBN (Print)9783031369988
DOIs
Publication statusPublished - 19 Jun 2023
Event41st IMAC, A Conference and Exposition on Structural Dynamics, 2023 - Austin, United States
Duration: 13 Feb 202316 Feb 2023

Publication series

NameConference Proceedings of the Society for Experimental Mechanics Series
ISSN (Print)2191-5644
ISSN (Electronic)2191-5652

Conference

Conference41st IMAC, A Conference and Exposition on Structural Dynamics, 2023
Country/TerritoryUnited States
CityAustin
Period13/02/2316/02/23

Keywords

  • Experimental nonlinear analysis
  • Meta-heuristic optimisation
  • Nonlinear dynamics
  • Nonlinear frequency response
  • Parameter identification

Fingerprint

Dive into the research topics of 'Experimental parameter identification of nonlinear mechanical systems via meta-heuristic optimisation methods'. Together they form a unique fingerprint.

Cite this