Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems
This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the r...
Gespeichert in:
| Veröffentlicht in: | Electronics (Basel) 2022-08, Vol.11 (16), p.2544 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 16 |
| container_start_page | 2544 |
| container_title | Electronics (Basel) |
| container_volume | 11 |
| creator | Yang, Haolin Kim, Bo-Gyu Oh, Jong-Seok Kim, Gi-Woo |
| description | This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the road roughness input influences the suspension system, AEKF-UI with a forgetting factor is proposed to simultaneously estimate the time-varying parameter (vehicle mass) of vehicle suspension systems and road roughness using an unknown input estimator. However, a constant forgetting factor does not adaptively weigh the covariance of all the states, and optimal filtering cannot be ensured. To resolve this problem, we present an adaptive forgetting factor technique employed to track time-varying parameters and unknown inputs. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate the vehicle mass variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm were verified through laboratory-level experiments. |
| doi_str_mv | 10.3390/electronics11162544 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2706179728</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A724446490</galeid><sourcerecordid>A724446490</sourcerecordid><originalsourceid>FETCH-LOGICAL-c361t-b237580013c302671907a72677b75ca8711164ded06c59dad4ab2f52ed3af6d23</originalsourceid><addsrcrecordid>eNptUUtPwzAMrhBIIOAXcInEeZBH2yzHgcZDDCEx4Fp5iTsCbTKSlMedH06mceCALdmW7c-fbBfFEaMnQih6ih3qFLyzOjLGal6V5Vaxx6lUI8UV3_4T7xaHMb7QLIqJsaB7xffc9kOXwKEfIpnGZHtI1jviW_KEz1Z3SG4hRgLOkEf36vyHI_ceTDbD8tlhLp1BREMyZmJglew7kulnQmdy8ga6Hhy5sF3CYN1yPXY-xBW6uCaZf8WEfTwodlroIh7--v3i8WL6cH41mt1dXp9PZiMtapZGCy5kNaaUCS0oryVTVILMgVzISsNYrtcvMy2tdaUMmBIWvK04GgFtbbjYL443c1fBvw0YU_Pih-AyZcMlrZlUko9z18mmawkdNta1PgXQWQ32VnuHrc35ieRlWdalohkgNgAdfIwB22YV8hnDV8Nos35R88-LxA9g2Yhd</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2706179728</pqid></control><display><type>article</type><title>Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><creator>Yang, Haolin ; Kim, Bo-Gyu ; Oh, Jong-Seok ; Kim, Gi-Woo</creator><creatorcontrib>Yang, Haolin ; Kim, Bo-Gyu ; Oh, Jong-Seok ; Kim, Gi-Woo</creatorcontrib><description>This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the road roughness input influences the suspension system, AEKF-UI with a forgetting factor is proposed to simultaneously estimate the time-varying parameter (vehicle mass) of vehicle suspension systems and road roughness using an unknown input estimator. However, a constant forgetting factor does not adaptively weigh the covariance of all the states, and optimal filtering cannot be ensured. To resolve this problem, we present an adaptive forgetting factor technique employed to track time-varying parameters and unknown inputs. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate the vehicle mass variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm were verified through laboratory-level experiments.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics11162544</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Embedded systems ; Estimation theory ; Kalman filtering ; Kalman filters ; Parameter estimation ; Parameters ; Roads ; Roads & highways ; Roughness ; Sensors ; South Korea ; Streets ; Suspension systems ; Vehicles</subject><ispartof>Electronics (Basel), 2022-08, Vol.11 (16), p.2544</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-b237580013c302671907a72677b75ca8711164ded06c59dad4ab2f52ed3af6d23</citedby><cites>FETCH-LOGICAL-c361t-b237580013c302671907a72677b75ca8711164ded06c59dad4ab2f52ed3af6d23</cites><orcidid>0000-0001-6976-6205 ; 0000-0003-4625-0382</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Yang, Haolin</creatorcontrib><creatorcontrib>Kim, Bo-Gyu</creatorcontrib><creatorcontrib>Oh, Jong-Seok</creatorcontrib><creatorcontrib>Kim, Gi-Woo</creatorcontrib><title>Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems</title><title>Electronics (Basel)</title><description>This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the road roughness input influences the suspension system, AEKF-UI with a forgetting factor is proposed to simultaneously estimate the time-varying parameter (vehicle mass) of vehicle suspension systems and road roughness using an unknown input estimator. However, a constant forgetting factor does not adaptively weigh the covariance of all the states, and optimal filtering cannot be ensured. To resolve this problem, we present an adaptive forgetting factor technique employed to track time-varying parameters and unknown inputs. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate the vehicle mass variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm were verified through laboratory-level experiments.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Embedded systems</subject><subject>Estimation theory</subject><subject>Kalman filtering</subject><subject>Kalman filters</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>Roads</subject><subject>Roads & highways</subject><subject>Roughness</subject><subject>Sensors</subject><subject>South Korea</subject><subject>Streets</subject><subject>Suspension systems</subject><subject>Vehicles</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUUtPwzAMrhBIIOAXcInEeZBH2yzHgcZDDCEx4Fp5iTsCbTKSlMedH06mceCALdmW7c-fbBfFEaMnQih6ih3qFLyzOjLGal6V5Vaxx6lUI8UV3_4T7xaHMb7QLIqJsaB7xffc9kOXwKEfIpnGZHtI1jviW_KEz1Z3SG4hRgLOkEf36vyHI_ceTDbD8tlhLp1BREMyZmJglew7kulnQmdy8ga6Hhy5sF3CYN1yPXY-xBW6uCaZf8WEfTwodlroIh7--v3i8WL6cH41mt1dXp9PZiMtapZGCy5kNaaUCS0oryVTVILMgVzISsNYrtcvMy2tdaUMmBIWvK04GgFtbbjYL443c1fBvw0YU_Pih-AyZcMlrZlUko9z18mmawkdNta1PgXQWQ32VnuHrc35ieRlWdalohkgNgAdfIwB22YV8hnDV8Nos35R88-LxA9g2Yhd</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Yang, Haolin</creator><creator>Kim, Bo-Gyu</creator><creator>Oh, Jong-Seok</creator><creator>Kim, Gi-Woo</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-6976-6205</orcidid><orcidid>https://orcid.org/0000-0003-4625-0382</orcidid></search><sort><creationdate>20220801</creationdate><title>Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems</title><author>Yang, Haolin ; Kim, Bo-Gyu ; Oh, Jong-Seok ; Kim, Gi-Woo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-b237580013c302671907a72677b75ca8711164ded06c59dad4ab2f52ed3af6d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Embedded systems</topic><topic>Estimation theory</topic><topic>Kalman filtering</topic><topic>Kalman filters</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>Roads</topic><topic>Roads & highways</topic><topic>Roughness</topic><topic>Sensors</topic><topic>South Korea</topic><topic>Streets</topic><topic>Suspension systems</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Haolin</creatorcontrib><creatorcontrib>Kim, Bo-Gyu</creatorcontrib><creatorcontrib>Oh, Jong-Seok</creatorcontrib><creatorcontrib>Kim, Gi-Woo</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</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><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Haolin</au><au>Kim, Bo-Gyu</au><au>Oh, Jong-Seok</au><au>Kim, Gi-Woo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems</atitle><jtitle>Electronics (Basel)</jtitle><date>2022-08-01</date><risdate>2022</risdate><volume>11</volume><issue>16</issue><spage>2544</spage><pages>2544-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>This study presents a vehicle mass estimation system based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested real-time methodology is based on the explicit correlation between road roughness and suspension system. Because the road roughness input influences the suspension system, AEKF-UI with a forgetting factor is proposed to simultaneously estimate the time-varying parameter (vehicle mass) of vehicle suspension systems and road roughness using an unknown input estimator. However, a constant forgetting factor does not adaptively weigh the covariance of all the states, and optimal filtering cannot be ensured. To resolve this problem, we present an adaptive forgetting factor technique employed to track time-varying parameters and unknown inputs. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate the vehicle mass variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm were verified through laboratory-level experiments.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics11162544</doi><orcidid>https://orcid.org/0000-0001-6976-6205</orcidid><orcidid>https://orcid.org/0000-0003-4625-0382</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2079-9292 |
| ispartof | Electronics (Basel), 2022-08, Vol.11 (16), p.2544 |
| issn | 2079-9292 2079-9292 |
| language | eng |
| recordid | cdi_proquest_journals_2706179728 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute |
| subjects | Accuracy Algorithms Embedded systems Estimation theory Kalman filtering Kalman filters Parameter estimation Parameters Roads Roads & highways Roughness Sensors South Korea Streets Suspension systems Vehicles |
| title | Simultaneous Estimation of Vehicle Mass and Unknown Road Roughness Based on Adaptive Extended Kalman Filtering of Suspension Systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T15%3A11%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Simultaneous%20Estimation%20of%20Vehicle%20Mass%20and%20Unknown%20Road%20Roughness%20Based%20on%20Adaptive%20Extended%20Kalman%20Filtering%20of%20Suspension%20Systems&rft.jtitle=Electronics%20(Basel)&rft.au=Yang,%20Haolin&rft.date=2022-08-01&rft.volume=11&rft.issue=16&rft.spage=2544&rft.pages=2544-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics11162544&rft_dat=%3Cgale_proqu%3EA724446490%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2706179728&rft_id=info:pmid/&rft_galeid=A724446490&rfr_iscdi=true |