An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension
Abstract Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of relia...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering Journal of automobile engineering, 2010-07, Vol.224 (7), p.829-848 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering |
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| creator | Metered, H Bonello, P Oyadiji, S O |
| description | Abstract
Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications. |
| doi_str_mv | 10.1243/09544070JAUTO1481 |
| format | Article |
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Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.</description><identifier>ISSN: 0954-4070</identifier><identifier>EISSN: 2041-2991</identifier><identifier>DOI: 10.1243/09544070JAUTO1481</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Active control ; Active damping ; Applied sciences ; Automobiles ; Automotive engineering ; Coils ; Comfort ; Computer simulation ; Control stability ; Control systems ; Controllers ; Criteria ; Dampers ; Dynamics ; Electric potential ; Exact sciences and technology ; Frequency domain analysis ; Ground, air and sea transportation, marine construction ; Hardware ; Hardware-in-the-loop simulation ; Machine components ; Magnetic levitation systems ; Mathematical analysis ; Mathematical models ; Mechanical engineering ; Mechanical engineering. Machine design ; Neural networks ; Reliability ; Sensors ; Simulation ; Springs and dampers ; Vehicles</subject><ispartof>Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering, 2010-07, Vol.224 (7), p.829-848</ispartof><rights>2010 Institution of Mechanical Engineers</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Professional Engineering Publishing Ltd 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-218372fc737b674f9347728f7f729b6f475ef20bb56a8318fd63e47e8e5ccb743</citedby><cites>FETCH-LOGICAL-c401t-218372fc737b674f9347728f7f729b6f475ef20bb56a8318fd63e47e8e5ccb743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1243/09544070JAUTO1481$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1243/09544070JAUTO1481$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23032820$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Metered, H</creatorcontrib><creatorcontrib>Bonello, P</creatorcontrib><creatorcontrib>Oyadiji, S O</creatorcontrib><title>An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension</title><title>Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering</title><description>Abstract
Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.</description><subject>Active control</subject><subject>Active damping</subject><subject>Applied sciences</subject><subject>Automobiles</subject><subject>Automotive engineering</subject><subject>Coils</subject><subject>Comfort</subject><subject>Computer simulation</subject><subject>Control stability</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Criteria</subject><subject>Dampers</subject><subject>Dynamics</subject><subject>Electric potential</subject><subject>Exact sciences and technology</subject><subject>Frequency domain analysis</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Hardware</subject><subject>Hardware-in-the-loop simulation</subject><subject>Machine components</subject><subject>Magnetic levitation systems</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mechanical engineering</subject><subject>Mechanical engineering. Machine design</subject><subject>Neural networks</subject><subject>Reliability</subject><subject>Sensors</subject><subject>Simulation</subject><subject>Springs and dampers</subject><subject>Vehicles</subject><issn>0954-4070</issn><issn>2041-2991</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kctOGzEUhq2qSKTAA7CzWlWsBnybsWcZRaUFRWID65HHOU6cesapPRPEro9eT4MqVBRvjqzz_f-5IXRJyTVlgt-QuhSCSHI_f3p8oELRD2jGiKAFq2v6Ec2mfDEBp-hTSluSnxTlDP2e99j1e0iDW-vBhek3BDxsAI8JcLC4hzFqn8PwHOLPhG2If9MJOldoM7g9YBP6IQY_4Rp3ep3hEDcQfFg7o71_wSvd7WCF97BxxmfxmHbQp1zvHJ1Y7RNcvMYz9HT77XHxo1g-fL9bzJeFEYQOBaOKS2aN5LKtpLA1F1IyZaWVrG4rK2QJlpG2LSutOFV2VXEQEhSUxrRS8DN0dfDdxfBrzPM2nUsGvNc9hDE1StTZg0qSyc__kdswxj4316iKCpr3PUFfjkG0JlLVVJAyU_RAmRhSimCbXXSdji8NJc10t-bd3bLm66uzTnl3NureuPRPyDjhTLGpg-sDl_Qa3lQ_avwHs_mmWg</recordid><startdate>20100701</startdate><enddate>20100701</enddate><creator>Metered, H</creator><creator>Bonello, P</creator><creator>Oyadiji, S O</creator><general>SAGE Publications</general><general>Sage Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8AF</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20100701</creationdate><title>An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension</title><author>Metered, H ; Bonello, P ; Oyadiji, S O</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-218372fc737b674f9347728f7f729b6f475ef20bb56a8318fd63e47e8e5ccb743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Active control</topic><topic>Active damping</topic><topic>Applied sciences</topic><topic>Automobiles</topic><topic>Automotive engineering</topic><topic>Coils</topic><topic>Comfort</topic><topic>Computer simulation</topic><topic>Control stability</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Criteria</topic><topic>Dampers</topic><topic>Dynamics</topic><topic>Electric potential</topic><topic>Exact sciences and technology</topic><topic>Frequency domain analysis</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Hardware</topic><topic>Hardware-in-the-loop simulation</topic><topic>Machine components</topic><topic>Magnetic levitation systems</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mechanical engineering</topic><topic>Mechanical engineering. Machine design</topic><topic>Neural networks</topic><topic>Reliability</topic><topic>Sensors</topic><topic>Simulation</topic><topic>Springs and dampers</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Metered, H</creatorcontrib><creatorcontrib>Bonello, P</creatorcontrib><creatorcontrib>Oyadiji, S O</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Metered, H</au><au>Bonello, P</au><au>Oyadiji, S O</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension</atitle><jtitle>Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering</jtitle><date>2010-07-01</date><risdate>2010</risdate><volume>224</volume><issue>7</issue><spage>829</spage><epage>848</epage><pages>829-848</pages><issn>0954-4070</issn><eissn>2041-2991</eissn><abstract>Abstract
Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1243/09544070JAUTO1481</doi><tpages>20</tpages></addata></record> |
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| ispartof | Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering, 2010-07, Vol.224 (7), p.829-848 |
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| subjects | Active control Active damping Applied sciences Automobiles Automotive engineering Coils Comfort Computer simulation Control stability Control systems Controllers Criteria Dampers Dynamics Electric potential Exact sciences and technology Frequency domain analysis Ground, air and sea transportation, marine construction Hardware Hardware-in-the-loop simulation Machine components Magnetic levitation systems Mathematical analysis Mathematical models Mechanical engineering Mechanical engineering. Machine design Neural networks Reliability Sensors Simulation Springs and dampers Vehicles |
| title | An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension |
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