Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization

Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization

This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic...

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Main Authors: Mohd Azraai, M. Razman, Priyandoko, Gigih, A. R., Yusoff
Format: Article
Language:English
Published: Trans Tech Publications, Switzerland 2014
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/1/12.pdf
id ump-5532
recordtype eprints
spelling ump-55322018-04-19T02:51:52Z http://umpir.ump.edu.my/id/eprint/5532/ Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization Mohd Azraai, M. Razman Priyandoko, Gigih A. R., Yusoff TS Manufactures This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic representation. The PSO algorithm specifically determines the best fit value and decrease marginal error which compare to the experimental data from various operating conditions in a given boundary. Trans Tech Publications, Switzerland 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5532/1/12.pdf Mohd Azraai, M. Razman and Priyandoko, Gigih and A. R., Yusoff (2014) Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization. Advanced Materials Research, 903. pp. 279-284. ISSN 1022-6680 (print), 1662-8985 (online) http://dx.doi.org/10.4028/www.scientific.net/AMR.903.279 DOI: 10.4028/www.scientific.net/AMR.903.279
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Mohd Azraai, M. Razman
Priyandoko, Gigih
A. R., Yusoff
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
description This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic representation. The PSO algorithm specifically determines the best fit value and decrease marginal error which compare to the experimental data from various operating conditions in a given boundary.
format Article
author Mohd Azraai, M. Razman
Priyandoko, Gigih
A. R., Yusoff
author_facet Mohd Azraai, M. Razman
Priyandoko, Gigih
A. R., Yusoff
author_sort Mohd Azraai, M. Razman
title Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
title_short Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
title_full Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
title_fullStr Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
title_full_unstemmed Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
title_sort bouc-wen model parameter identification for a mr fluid damper using particle swarm optimization
publisher Trans Tech Publications, Switzerland
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/
http://umpir.ump.edu.my/id/eprint/5532/1/12.pdf
first_indexed 2023-09-18T22:00:52Z
last_indexed 2023-09-18T22:00:52Z
_version_ 1777414388624392192

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