Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper
This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system's mathematical model is established...
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| Format: | Article |
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SAGE Publications Inc.
2017
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| _version_ | 1796862568971632640 |
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| author | Ab. Talib, M. H. Mat Darus, I. Z. |
| author_facet | Ab. Talib, M. H. Mat Darus, I. Z. |
| author_sort | Ab. Talib, M. H. |
| collection | ePrints |
| description | This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system's mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc-Wen model of a MR damper is used to determine the required damping force based on force-displacement and force-velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL-PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis. |
| first_indexed | 2024-03-05T20:13:36Z |
| format | Article |
| id | utm.eprints-76770 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:13:36Z |
| publishDate | 2017 |
| publisher | SAGE Publications Inc. |
| record_format | dspace |
| spelling | utm.eprints-767702018-05-31T09:28:01Z http://eprints.utm.my/76770/ Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper Ab. Talib, M. H. Mat Darus, I. Z. TJ Mechanical engineering and machinery This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system's mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc-Wen model of a MR damper is used to determine the required damping force based on force-displacement and force-velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL-PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis. SAGE Publications Inc. 2017 Article PeerReviewed Ab. Talib, M. H. and Mat Darus, I. Z. (2017) Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper. JVC/Journal of Vibration and Control, 23 (3). pp. 501-514. ISSN 1077-5463 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011537421&doi=10.1177%2f1077546315580693&partnerID=40&md5=200e3543f0ad55854eb32f2af1c94f9e DOI:10.1177/1077546315580693 |
| spellingShingle | TJ Mechanical engineering and machinery Ab. Talib, M. H. Mat Darus, I. Z. Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title | Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title_full | Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title_fullStr | Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title_full_unstemmed | Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title_short | Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper |
| title_sort | intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi active suspension system using magneto rheological damper |
| topic | TJ Mechanical engineering and machinery |
| work_keys_str_mv | AT abtalibmh intelligentfuzzylogicwithfireflyalgorithmandparticleswarmoptimizationforsemiactivesuspensionsystemusingmagnetorheologicaldamper AT matdarusiz intelligentfuzzylogicwithfireflyalgorithmandparticleswarmoptimizationforsemiactivesuspensionsystemusingmagnetorheologicaldamper |