Advanced Stewart Flight Simulator Motion Cueing Algorithm Design with Parallel Architectures and Adaptive Switching Mechanism

Advanced Stewart Flight Simulator Motion Cueing Algorithm Design with Parallel Architectures and Adaptive Switching Mechanism | 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 Advanced Stewart Flight Simulator Motion Cueing Algorithm Design with Parallel Architectures and Adaptive Switching Mechanism Jiangwei Zhao, Zhengjia Xu, Haoman Gu, Zhiqing Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6253062/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 With the excellent mechanism characteristics of high stiffness and high maneuverability, the 6-Degree-of-Freedom (DoF) Stewart platform has been widely applied to build flight simulator platforms for replicating the motion sensation during pilot training. The fidelity of dynamic simulation depends on the quality of the Motion Cueing Algorithm (MCA). However, conventional MCAs neglect the simulator operating boundary constraints and demonstrate limitations in handling multi-constraint problems. In this paper, a MCA method for Switchable Nonlinear Model Predictive Control (S-NMPC)-based on parallel architecture and Adaptive Switching Mechanism(ASM) is proposed to guarantee solution availability and reduce control errors due to the differences in the terminal state constraints (TSCs) of NMPCs. Within the operating range of the simulator, accurate tracking can be achieved by an NMPC-based MCA, whose TSC has an Equational Constraint (EC); Beyond the operating range of the simulator, optimal approximate tracking can be achieved by a NMPC-based MCA, whose TSC does not possess EC. Moreover, due to the use of the Extended Kalman Filter (EKF) in the ASM, the optimality and smoothness of the system when switching between multiple modes of operation is ensured. Additionally, a method for assessing the fidelity of kinetic simulation based on Multiple Index Fusion Weighting (MIFW) was established by combining the characteristics of objective and subjective assessment. A horizontal stall experiment meeting the MIFW evaluation criteria suggests that the proposed S-NMPC-based MCA was 11.63% and 37.57% higher than the NMPC-based MCA and Classical Washout Filter (CWF)-based MCA, respectively. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Mechanical engineering Stewart Platform Motion Cueing Algorithm Nonlinear Model Predictive Control Multiple Index Fusion Weighting 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-6253062","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443854205,"identity":"f05d5055-ed79-4320-b0ce-337e5972880f","order_by":0,"name":"Jiangwei Zhao","email":"","orcid":"","institution":"Pingdingshan University","correspondingAuthor":false,"prefix":"","firstName":"Jiangwei","middleName":"","lastName":"Zhao","suffix":""},{"id":443854206,"identity":"579367a8-2ae0-40a2-8705-0e833b151c19","order_by":1,"name":"Zhengjia Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCRDBAyYZDnwwsLEjSQvjwRkFaclEaoEA5sM8Hw4xNhDSIT+7+djDLzIW8gwSOQaHbQwOMDOwHz66AZ8WxjnH0o1leCQMG3jOGBzOMbjDx8CTlnYDnxZmiRwzaQkeCcYG9h6QlmfMDBI8Zni1sEnkfwNpsW9g5jE4bGFwmLGBkBYeiRw2yQ88EolgWxiI0SIhkWYmDdSY3MBzrOBgj0FaMhshv8jPSH4m+bOnzrZBInnzhx9/bOz42Q8fw6sFBJh5exgY7A/AfEdIOQgw/vhBjLJRMApGwSgYsQAA+sNDV02PhT4AAAAASUVORK5CYII=","orcid":"","institution":"Cranfield University","correspondingAuthor":true,"prefix":"","firstName":"Zhengjia","middleName":"","lastName":"Xu","suffix":""},{"id":443854208,"identity":"3f3a7927-df8f-4c75-a872-c26e43652f75","order_by":2,"name":"Haoman Gu","email":"","orcid":"","institution":"Pingdingshan University","correspondingAuthor":false,"prefix":"","firstName":"Haoman","middleName":"","lastName":"Gu","suffix":""},{"id":443854209,"identity":"7e10a776-aa20-466d-92a9-eb517505ccc2","order_by":3,"name":"Zhiqing Zhou","email":"","orcid":"","institution":"Pingdingshan University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqing","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-03-18 12:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6253062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6253062/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94471044,"identity":"58e4ab83-5b46-4568-800c-3569db2e1589","added_by":"auto","created_at":"2025-10-27 15:35:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":780588,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6253062/v1_covered_57bae8f4-b022-487b-ad5d-ac74094f7430.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Stewart Flight Simulator Motion Cueing Algorithm Design with Parallel Architectures and Adaptive Switching Mechanism","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":"Stewart Platform, Motion Cueing Algorithm, Nonlinear Model Predictive Control, Multiple Index Fusion Weighting","lastPublishedDoi":"10.21203/rs.3.rs-6253062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6253062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the excellent mechanism characteristics of high stiffness and high maneuverability, the 6-Degree-of-Freedom (DoF) Stewart platform has been widely applied to build flight simulator platforms for replicating the motion sensation during pilot training. The fidelity of dynamic simulation depends on the quality of the Motion Cueing Algorithm (MCA). However, conventional MCAs neglect the simulator operating boundary constraints and demonstrate limitations in handling multi-constraint problems. In this paper, a MCA method for Switchable Nonlinear Model Predictive Control (S-NMPC)-based on parallel architecture and Adaptive Switching Mechanism(ASM) is proposed to guarantee solution availability and reduce control errors due to the differences in the terminal state constraints (TSCs) of NMPCs. Within the operating range of the simulator, accurate tracking can be achieved by an NMPC-based MCA, whose TSC has an Equational Constraint (EC); Beyond the operating range of the simulator, optimal approximate tracking can be achieved by a NMPC-based MCA, whose TSC does not possess EC. Moreover, due to the use of the Extended Kalman Filter (EKF) in the ASM, the optimality and smoothness of the system when switching between multiple modes of operation is ensured. Additionally, a method for assessing the fidelity of kinetic simulation based on Multiple Index Fusion Weighting (MIFW) was established by combining the characteristics of objective and subjective assessment. 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