Research of the LQR algorithm with constraints based on preview control

Research of the LQR algorithm with constraints based on preview control | 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 Article Research of the LQR algorithm with constraints based on preview control Yang Jin, Qi Ouyang, Rui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2961763/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 This study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. The two-degree-of-freedom dynamic model of the vehicle and the road-vehicle error dynamic model is established, and the future road curvature is incorporated as a disturbance into the LQR state vector using the preview control theory. An augmented LQR problem is solved according to the optimal theory to obtain the analytical solution of the control quantity. This strategy also enhances the adaptive ability of the intelligent vehicle to extreme conditions by taking into account the dynamic constraints. The preview time is optimized using the simulated annealing algorithm to obtain the optimal preview time under different vehicle speeds and road friction coefficients. The stability of the closed-loop control system composed of the new algorithm is analyzed to verify its feasibility. Simulation results on the Carsim/Simulink joint platform demonstrate that it has excellent trajectory tracking ability, stability, and robustness to the vehicle speed. The proposed strategy has the potential to significantly advance the field of intelligent vehicle control and improve the safety and efficiency of transportation systems. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Electrical and electronic engineering 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. 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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-2961763","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":210020548,"identity":"95c2628f-d2e2-4f64-9104-f04463070dee","order_by":0,"name":"Yang Jin","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Jin","suffix":""},{"id":210020549,"identity":"42e1965e-f430-4ac5-9db6-83817a41aed8","order_by":1,"name":"Qi Ouyang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYNACAxsIzUOCljSStTAcJkGLwfGzh1+8KTgvz3cjgfHB2zYGeXOCWs7kpVnOMbhtOPNGArPh3DYGw50NhLQcyDEz5jG4nWBwI4FNmreNIcHgACEt59+AtJwDaWH/TZyWGznGj3kMDoBtYSZKi+SNN2aMcwySDWeeedgsOeechOEGQlr4zucYf3jzx06e73jywQ9vymzkCdqicICBTQIcHQcYG4CkBAH1QCDfwMD8AaKFsOJRMApGwSgYoQAAZWlCdNIHDx8AAAAASUVORK5CYII=","orcid":"","institution":"Chongqing University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Ouyang","suffix":""},{"id":210020550,"identity":"9d9eaf0f-dbfd-4fc4-a878-e14c8cef7f7f","order_by":2,"name":"Rui Li","email":"","orcid":"","institution":"Chongqing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-05-21 06:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2961763/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2961763/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45588549,"identity":"e7a45414-7bb0-4fd6-a9d2-9ba24ec08a1f","added_by":"auto","created_at":"2023-10-31 20:38:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1739705,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript230614.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2961763/v1_covered_44dcf20d-ad25-47c6-bad3-718c9e538b67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research of the LQR algorithm with constraints based on preview control","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":"","lastPublishedDoi":"10.21203/rs.3.rs-2961763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2961763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. 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