Dual-Mode Model Predictive Motion Cueing

Dual-Mode Model Predictive Motion Cueing | 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 Dual-Mode Model Predictive Motion Cueing Tim Nicolai, Sebastian Emmerich, Michael Burger, Matthias Gerdts This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8743301/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Dynamic driving simulators rely on motion cueing algorithms to generate platform trajectories that reproduce accelerations and rotations of simulated vehicles within physical limits. The employment of model predictive control (MPC) has emerged as a promising approach for this task, primarily due to its capability to address constraints explicitly. However, ensuring closed-loop stability poses a fundamental challenge. Conventional stability-enforcing mechanisms, such as terminal equality constraints or terminal costs, limit workspace utilization by inducing premature washout behavior, while extending the prediction horizon conflicts with stringent real-time requirements. This paper presents a dual-mode architecture that decouples stability certification from performance optimization. A safety mode based on linear state feedback defines a maximal positive invariant set that guarantees asymptotic stability and constraint satisfaction. Within this set, a performance mode employs MPC for reference tracking without terminal constraints, maximizing workspace utilization. The approach is formulated as a quadratic program and validated on a robot-based driving simulator. Simulation results confirm guaranteed constraint satisfaction and safe mode switching capability. The architecture reconciles stability certification with performance optimization for MPC-based motion cueing. Driving simulation Motion cueing Model predictive control Stability analysis Invariant sets Quadratic programming Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 01 Feb, 2026 First submitted to journal 30 Jan, 2026 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. 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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-8743301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602071244,"identity":"6a5d6f96-189c-47b4-bb4d-a1cf3d9e0be8","order_by":0,"name":"Tim Nicolai","email":"data:image/png;base64,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","orcid":"","institution":"Fraunhofer Institute for Industrial Mathematics ITWM","correspondingAuthor":true,"prefix":"","firstName":"Tim","middleName":"","lastName":"Nicolai","suffix":""},{"id":602071245,"identity":"746a26bc-4c0f-464a-bf62-5a6a3053feed","order_by":1,"name":"Sebastian Emmerich","email":"","orcid":"","institution":"Fraunhofer Institute for Industrial Mathematics ITWM","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Emmerich","suffix":""},{"id":602071246,"identity":"9f7db008-0e89-4209-a546-638b98332fed","order_by":2,"name":"Michael Burger","email":"","orcid":"","institution":"Fraunhofer Institute for Industrial Mathematics ITWM","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Burger","suffix":""},{"id":602071247,"identity":"5c262c47-f79f-46d3-94ee-96687081c796","order_by":3,"name":"Matthias Gerdts","email":"","orcid":"","institution":"University of the Bundeswehr Munich","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Gerdts","suffix":""}],"badges":[],"createdAt":"2026-01-30 16:25:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8743301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8743301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780728,"identity":"4759c4ca-f8b7-49c8-8857-a839e2c85b38","added_by":"auto","created_at":"2026-03-17 07:53:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1119765,"visible":true,"origin":"","legend":"","description":"","filename":"KLAIM2025DualModeModelPredictiveMotionCueingNicolai.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8743301/v1_covered_4bfadcb8-7b14-4fe0-b5c0-37aee00d4aaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-Mode Model Predictive Motion Cueing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"computational-science-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Computational Science and Engineering](https://link.springer.com/journal/44207)","snPcode":"44207","submissionUrl":"https://submission.springernature.com/new-submission/44207/3","title":"Computational Science and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Driving simulation, Motion cueing, Model predictive control, Stability analysis, Invariant sets, Quadratic programming","lastPublishedDoi":"10.21203/rs.3.rs-8743301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8743301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Dynamic driving simulators rely on motion cueing algorithms to generate platform trajectories that reproduce accelerations and rotations of simulated vehicles within physical limits. 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