Finite-time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network

Finite-time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network | 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 Finite-time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network Yang Li, Yang Ting This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3831851/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 In this paper, a composite finite-time prescribed performance tracking control scheme is presented for an unmanned helicopter (UH) system subject to performance constraints, model uncertainties and external perturbations. A new finite-time neural network disturbance observer (FTNNDO) with adaptive laws is designed to deal with the external disturbances and model uncertainties, which not only realizes the fast convergence rate in finite time, but also eliminates the complicated differential calculation in the traditional backstepping technique. Using the continuous adaptive law, the neural network (NN) approximate errors can be effectively estimated and compensated online without chattering and gain overestimation caused by traditional methods, thus further enhancing the robustness of the system. To constrain the tracking performance of the transient process and steady-state accuracy, a novel prescribed performance function is designed to preset the tracking errors within prescribed boundaries. Based on the FTNNDO and barrier Lyapunov function (BLF), an improved finite-time tracking controller is designed to achieve fast convergence with prescribed performance. By using Lyapunov synthesis, it is strictly proved that the finite-time convergence of the closed-loop control system can be achieved and tracking errors are always within the prescribed performance bounds. In the end, simulation results for UH tracking control system are given to demonstrate the effectiveness of developed control scheme. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Engineering/Aerospace 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. 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-3831851","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265722528,"identity":"53511ae9-40da-42ef-a909-1184dffe253f","order_by":0,"name":"Yang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACPmYEm/FBQkUNYS1sSFqYDR6cOUaEFmS25MMWZpwqEcrYeQw/F/xiyDO43WNWkdjAxsDf3p1AwGE8xtIz+xiKDe6cMbuRuEOGQeLM2Q2EtBhI8/YwJG67kQPUcoaNwUAil6AW498wLQWJbcxEaTGT5vkB0cJApBa2MmveBobE/XeOFUsknDnGQ9Av/PyHN9/m+cOQOHN288aPPypq5Pjbe/FrYWDgMGBgbPvPwCAB4fIQUA4C7A8YGP4wwLWMglEwCkbBKMAAANLCQxEylrATAAAAAElFTkSuQmCC","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":265722529,"identity":"8aebae61-54be-4870-b42f-db41d8f544f8","order_by":1,"name":"Yang Ting","email":"","orcid":"","institution":"Beijing Aerospace Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Ting","suffix":""}],"badges":[],"createdAt":"2024-01-03 12:50:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3831851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3831851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56787190,"identity":"85647572-38d0-435f-95fb-bedf6f80a67f","added_by":"auto","created_at":"2024-05-20 13:03:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861936,"visible":true,"origin":"","legend":"","description":"","filename":"FinitetimePrescribedPerformanceTrackingControlforUnmannedHelicopterSystemUsingNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3831851/v1_covered_3e0ac067-12cc-4593-87e3-bf403857d834.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Finite-time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network","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-3831851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3831851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, a composite finite-time prescribed performance tracking control scheme is presented for an unmanned helicopter (UH) system subject to performance constraints, model uncertainties and external perturbations. A new finite-time neural network disturbance observer (FTNNDO) with adaptive laws is designed to deal with the external disturbances and model uncertainties, which not only realizes the fast convergence rate in finite time, but also eliminates the complicated differential calculation in the traditional backstepping technique. Using the continuous adaptive law, the neural network (NN) approximate errors can be effectively estimated and compensated online without chattering and gain overestimation caused by traditional methods, thus further enhancing the robustness of the system. To constrain the tracking performance of the transient process and steady-state accuracy, a novel prescribed performance function is designed to preset the tracking errors within prescribed boundaries. Based on the FTNNDO and barrier Lyapunov function (BLF), an improved finite-time tracking controller is designed to achieve fast convergence with prescribed performance. By using Lyapunov synthesis, it is strictly proved that the finite-time convergence of the closed-loop control system can be achieved and tracking errors are always within the prescribed performance bounds. In the end, simulation results for UH tracking control system are given to demonstrate the effectiveness of developed control scheme.\u003c/p\u003e","manuscriptTitle":"Finite-time Prescribed Performance Tracking Control for Unmanned Helicopter System Using Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 06:26:41","doi":"10.21203/rs.3.rs-3831851/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":"7a47d117-bb99-4ac0-83fd-db1da1574b74","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27994427,"name":"Physical sciences/Mathematics and computing/Applied mathematics"},{"id":27994428,"name":"Physical sciences/Engineering/Aerospace engineering"}],"tags":[],"updatedAt":"2024-05-20T12:55:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 06:26:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3831851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3831851","identity":"rs-3831851","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}