Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models
<p>The 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 carefull...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2021
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513560301797376 |
|---|---|
| author | Asan G. A. Muthalif (16888818) |
| author2 | M. Khusyaie M. Razali (16891491) N. H. Diyana Nordin (16891494) Syamsul Bahrin Abdul Hamid (16891497) |
| author2_role | author author author |
| author_facet | Asan G. A. Muthalif (16888818) M. Khusyaie M. Razali (16891491) N. H. Diyana Nordin (16891494) Syamsul Bahrin Abdul Hamid (16891497) |
| author_role | author |
| dc.creator.none.fl_str_mv | Asan G. A. Muthalif (16888818) M. Khusyaie M. Razali (16891491) N. H. Diyana Nordin (16891494) Syamsul Bahrin Abdul Hamid (16891497) |
| dc.date.none.fl_str_mv | 2021-05-14T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3080432 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Parametric_Estimation_From_Empirical_Data_Using_Particle_Swarm_Optimization_Method_for_Different_Magnetorheological_Damper_Models/24042453 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Fluid mechanics and thermal engineering Mechanical engineering Information and computing sciences Data management and data science Distributed computing and systems software Shock absorbers Particle swarm optimization Magnetic hysteresis Genetic algorithms Estimation Fluids Optimization Magnetorheological fluid damper Parametric estimation Genetic algorithm |
| dc.title.none.fl_str_mv | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3080432" target="_blank">https://dx.doi.org/10.1109/access.2021.3080432</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4b8c2749cfe32c5172d404a569de8be7 |
| identifier_str_mv | 10.1109/access.2021.3080432 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042453 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper ModelsAsan G. A. Muthalif (16888818)M. Khusyaie M. Razali (16891491)N. H. Diyana Nordin (16891494)Syamsul Bahrin Abdul Hamid (16891497)EngineeringFluid mechanics and thermal engineeringMechanical engineeringInformation and computing sciencesData management and data scienceDistributed computing and systems softwareShock absorbersParticle swarm optimizationMagnetic hysteresisGenetic algorithmsEstimationFluidsOptimizationMagnetorheological fluid damperParametric estimationGenetic algorithm<p>The 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3080432" target="_blank">https://dx.doi.org/10.1109/access.2021.3080432</a></p>2021-05-14T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3080432https://figshare.com/articles/journal_contribution/Parametric_Estimation_From_Empirical_Data_Using_Particle_Swarm_Optimization_Method_for_Different_Magnetorheological_Damper_Models/24042453CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240424532021-05-14T00:00:00Z |
| spellingShingle | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models Asan G. A. Muthalif (16888818) Engineering Fluid mechanics and thermal engineering Mechanical engineering Information and computing sciences Data management and data science Distributed computing and systems software Shock absorbers Particle swarm optimization Magnetic hysteresis Genetic algorithms Estimation Fluids Optimization Magnetorheological fluid damper Parametric estimation Genetic algorithm |
| status_str | publishedVersion |
| title | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| title_full | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| title_fullStr | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| title_full_unstemmed | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| title_short | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| title_sort | Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models |
| topic | Engineering Fluid mechanics and thermal engineering Mechanical engineering Information and computing sciences Data management and data science Distributed computing and systems software Shock absorbers Particle swarm optimization Magnetic hysteresis Genetic algorithms Estimation Fluids Optimization Magnetorheological fluid damper Parametric estimation Genetic algorithm |