Vehicle sideslip angle estimation under tire nonlinearity: A nonlinear disturbance estimator with experimental validation

Vehicle sideslip angle estimation under tire nonlinearity: A nonlinear disturbance estimator with experimental validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vehicle sideslip angle estimation under tire nonlinearity: A nonlinear disturbance estimator with experimental validation Zhengwu Fan, Jiaqi Lyu, Wenbo Liu, Jiahao Pei, Zhifei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9103372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate estimation of the vehicle sideslip angle at nonlinear driving conditions is important for the vehicle stability control systems. A nonlinear disturbance estimator (NDE) that is designed for real-time implementation on production vehicle electronic control units is presented in this paper. A seven-degree-of-freedom vehicle model is first developed with an explicit consideration of vertical tire force variations to capture load‑transfer‑induced nonlinear tire characteristics, which is an important component affecting the vehicle stability during extreme maneuvers. The NDE is constructed using an adaptive square‑root cubature Kalman filter (ASRCKF) which recursively updates noise covariances to keep robustness under different road conditions. The estimator is experimentally validated by double‑lane change tests on dry asphalt and slalom tests on low‑friction icy surfaces, with results quantitatively compared against a conventional linear disturbance estimator (LDE). The NDE decreases root mean square error by 42% on dry asphalt and 66% on ice, while the maximum absolute error—critical for safety applications—is decreased by 42% and 49%, respectively. The algorithm is proven by the computational analysis to be adequate to implement in real‑time with 2.8 ms per iteration. These results show that the proposed NDE provides reliable performance in extreme conditions, which is a practical solution to improving vehicle stability control. Vehicle' sideslip angle estimation Nonlinear disturbance estimator Adaptive Kalman filtering Experimental validation Vehicle stability control Tire nonlinearity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9103372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607642860,"identity":"396014ff-56cf-4643-b3a4-58db639a4ca4","order_by":0,"name":"Zhengwu Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBAC9gYGBmYIk/nAgQ8VRGjhOQDXwpZ4cMYZ0rTwGB/mbSFGC/vZw58LKu7YbbiR8+EAbwODPL/YAQJaePISjGeceZa84UbuhgOSOxgMZ85OwK/FniHHIJm37XCyGUiL4RmGBIPbBLTw8L8xOMz7D6Ql58GBxDZitEjkGDbzNhy2A2phOHCQOC1vjJl5jh1OsD/zzOBgwxkJwn7h4c8x/sxTc9hesj358ec/FTby/NIEtMBAYgOEliBOOQjYE690FIyCUTAKRhwAAHxwSmemy0kaAAAAAElFTkSuQmCC","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhengwu","middleName":"","lastName":"Fan","suffix":""},{"id":607642866,"identity":"cfc79895-13bc-45a2-b2c1-232a7b91f7f7","order_by":1,"name":"Jiaqi Lyu","email":"","orcid":"","institution":"Stevens Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Lyu","suffix":""},{"id":607642868,"identity":"ee2ae5c2-70db-4a2e-a52d-ac1462b26ba7","order_by":2,"name":"Wenbo Liu","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Liu","suffix":""},{"id":607642870,"identity":"243958bb-5592-497c-ba46-f3d9b780acea","order_by":3,"name":"Jiahao Pei","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Pei","suffix":""},{"id":607642871,"identity":"eab90805-706a-42ca-b059-617a21602ef6","order_by":4,"name":"Zhifei Wu","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhifei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-12 10:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9103372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9103372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106542795,"identity":"a7a2b99e-982c-4850-8571-7548e7b686cb","added_by":"auto","created_at":"2026-04-09 16:25:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1353361,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9103372/v1_covered_d2747226-c887-4b90-8bb0-b1b4a9fef0ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vehicle sideslip angle estimation under tire nonlinearity: A nonlinear disturbance estimator with experimental validation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vehicle' sideslip angle estimation, Nonlinear disturbance estimator, Adaptive Kalman filtering, Experimental validation, Vehicle stability control, Tire nonlinearity","lastPublishedDoi":"10.21203/rs.3.rs-9103372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9103372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of the vehicle sideslip angle at nonlinear driving conditions is important for the vehicle stability control systems. A nonlinear disturbance estimator (NDE) that is designed for real-time implementation on production vehicle electronic control units is presented in this paper. A seven-degree-of-freedom vehicle model is first developed with an explicit consideration of vertical tire force variations to capture load‑transfer‑induced nonlinear tire characteristics, which is an important component affecting the vehicle stability during extreme maneuvers. The NDE is constructed using an adaptive square‑root cubature Kalman filter (ASRCKF) which recursively updates noise covariances to keep robustness under different road conditions. The estimator is experimentally validated by double‑lane change tests on dry asphalt and slalom tests on low‑friction icy surfaces, with results quantitatively compared against a conventional linear disturbance estimator (LDE). The NDE decreases root mean square error by 42% on dry asphalt and 66% on ice, while the maximum absolute error\u0026mdash;critical for safety applications\u0026mdash;is decreased by 42% and 49%, respectively. The algorithm is proven by the computational analysis to be adequate to implement in real‑time with 2.8 ms per iteration. These results show that the proposed NDE provides reliable performance in extreme conditions, which is a practical solution to improving vehicle stability control.\u003c/p\u003e","manuscriptTitle":"Vehicle sideslip angle estimation under tire nonlinearity: A nonlinear disturbance estimator with experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 12:47:09","doi":"10.21203/rs.3.rs-9103372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70e49592-376d-467d-b888-9c16f072ebd7","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T16:25:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 12:47:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9103372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9103372","identity":"rs-9103372","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}