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AuthorMuthalif, Asan G. A.
AuthorRazali, M. Khusyaie M.
AuthorNordin, N. H. Diyana
AuthorHamid, Syamsul Bahrin Abdul
Available date2024-05-14T03:51:42Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3080432
ISSN21693536
URIhttp://hdl.handle.net/10576/54942
AbstractThe nonlinearity behaviour of magnetorheological fluid (MRF) can be described using a number of established models such as Bingham and Modified Bouc-Wen models. Since these models require the identification of model parameters, there is a need to estimate the parameters' value carefully. In this paper, an optimization algorithm, i.e., the Particle Swarm Optimization (PSO) algorithm, is utilized to identify the models' parameters. The PSO algorithm distinctively controls the best fit value by minimizing marginal error through root-mean-square error between the models and the empirical response. The validation of the algorithm is attained by comparing the resulting modified Bouc-Wen model behaviour using PSO against the same model's behaviour, identified using Genetic Algorithm (GA). The validation results indicate that the application of PSO is better in identifying the model parameters. Results from this estimation can be used to design a controller for various applications such as prosthetic limbs.
SponsorThis work was supported in part by the Exploratory Research Grant Scheme from the Ministry of Higher Education Malaysia under Grant ERGS13-020-0053, and in part by the Qatar University-International Research Collaboration under Grant IRCC-2020-017.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectgenetic algorithm
Magnetorheological fluid damper
parametric estimation
particle swarm optimization
TitleParametric Estimation from Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models
TypeArticle
Pagination72602-72613
Volume Number9
dc.accessType Open Access


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