A Model Predictive Control for Steady-state Drifting and Tracking of Electronic-Two-Rear-Wheel Drive Automobiles

A Model Predictive Control for Steady-state Drifting and Tracking of Electronic-Two-Rear-Wheel Drive Automobiles | 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 A Model Predictive Control for Steady-state Drifting and Tracking of Electronic-Two-Rear-Wheel Drive Automobiles Lei He, Yixiao Wang, Zhenhai Gao, Guoying Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1719854/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 Drifting enables the vehicle to quickly adjust its posture and velocity, with the potential to enhance vehicle safety and maneuverability in extreme operating conditions. To improve the handling stability and tracking accuracy of drift control, a controller framework is presented based on model predictive control theory for electronic-two-rear-wheel drive (e-2RWD) automobiles. Primarily, the non-linear bicycle dynamics model is constructed, and in which the motion relationship between the vehicle and the specified trajectory is revealed.Then, a set of reference states are solved for the specified trajectory and desired sideslip. Finally, the MPC drift controller is used to work in conjunction with the wheel speed closed-loop controller to provide coordinated control of the steering angle and both rear wheel drive torques. The performance of the proposed drift controller is assessed in software simulations in two high sideslip driving scenarios with different trajectories. The simulation results show that the proposed controller is able to rapidly enter the steady-state drifting and effectively reduce the fluctuations of vehicle states, striking a reasonable balance between satisficing the accuracy demands and keeping vehicle handling stability. The major contribution of this paper is the first application of the MPC and wheel speed controller combined operation mode to drift control. Due to the advantage of proposed method predicting the vehicle's future dynamic behavior in advance, the controller outputs can be effectively constrained and optimized. Electronic-two-rear-wheel drive automobiles Autonomous vehicles Model predictive control Steady-state drifting Trajectory tracking Distributed driving Full Text 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-1719854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":117442464,"identity":"1ae6af00-44e8-4d97-b227-01542e554f1f","order_by":0,"name":"Lei He","email":"","orcid":"","institution":"Jilin University State Key Laboratory of Automotive Simulation and Control","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"He","suffix":""},{"id":117442465,"identity":"b21f1145-859e-4ea6-91ae-354cb116c8bd","order_by":1,"name":"Yixiao Wang","email":"","orcid":"https://orcid.org/0000-0003-2262-8256","institution":"Jilin University State Key Laboratory of Automotive Simulation and Control","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yixiao","middleName":"","lastName":"Wang","suffix":""},{"id":117442466,"identity":"eb00fcd5-6384-443c-9dbc-d312f30dae71","order_by":2,"name":"Zhenhai Gao","email":"","orcid":"","institution":"Jilin University State Key Laboratory of Automotive Simulation and Control","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenhai","middleName":"","lastName":"Gao","suffix":""},{"id":117442467,"identity":"15507986-e353-4efb-8412-8f202fe8adc7","order_by":3,"name":"Guoying Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYLCCBAMJOTb29gMkaPlQYGHMx3MmgXgdjDM+VCTOk3AwIE45f/8aA2YeA4n0NgmGBIYfFdsIa5G48QasJbdNuvEAY8+Z24S1GEicMf8N1iJzIIGZsY04LRCHsUkkGBCphb/HgHGGgUQC8VokbrAVMHwwkDBsAwbyQaL8wt9/eANDwp86efn29oMPflQQoYVBIgMRHQeIUA+y5vgD4hSOglEwCkbByAUA1Q43UpYEr00AAAAASUVORK5CYII=","orcid":"","institution":"Jilin University State Key Laboratory of Automotive Simulation and Control","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guoying","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-06-02 14:37:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1719854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1719854/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23546471,"identity":"00cc2635-7253-408b-8f81-a75905c7f63c","added_by":"auto","created_at":"2022-07-06 17:55:04","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1757651,"visible":true,"origin":"","legend":"","description":"","filename":"AModelPredictiveControlforDriftingandTracking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1719854/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"A Model Predictive Control for Steady-state Drifting and Tracking of Electronic-Two-Rear-Wheel Drive Automobiles","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1719854/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":true,"isPdf":false,"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":"Electronic-two-rear-wheel drive automobiles, Autonomous vehicles, Model predictive control, Steady-state drifting, Trajectory tracking, Distributed driving","lastPublishedDoi":"10.21203/rs.3.rs-1719854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1719854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Drifting enables the vehicle to quickly adjust its posture and velocity, with the potential to enhance vehicle safety and maneuverability in extreme operating conditions. To improve the handling stability and tracking accuracy of drift control, a controller framework is presented based on model predictive control theory for electronic-two-rear-wheel drive (e-2RWD) automobiles. Primarily, the non-linear bicycle dynamics model is constructed, and in which the motion relationship between the vehicle and the specified trajectory is revealed.Then, a set of reference states are solved for the specified trajectory and desired sideslip. Finally, the MPC drift controller is used to work in conjunction with the wheel speed closed-loop controller to provide coordinated control of the steering angle and both rear wheel drive torques. The performance of the proposed drift controller is assessed in software simulations in two high sideslip driving scenarios with different trajectories. The simulation results show that the proposed controller is able to rapidly enter the steady-state drifting and effectively reduce the fluctuations of vehicle states, striking a reasonable balance between satisficing the accuracy demands and keeping vehicle handling stability. The major contribution of this paper is the first application of the MPC and wheel speed controller combined operation mode to drift control. 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