Command Filter-Based Adaptive NN Fixed-Time Prescribed Performance Control for Nonstrict-Feedback Nonlinear Systems

Command Filter-Based Adaptive NN Fixed-Time Prescribed Performance Control for Nonstrict-Feedback Nonlinear Systems | 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 Command Filter-Based Adaptive NN Fixed-Time Prescribed Performance Control for Nonstrict-Feedback Nonlinear Systems Xiaoli Yang, Jing Li, Shuzhi Sam Ge, Xiaoling Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2206193/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 A command filter-based adaptive tracking control strategy is designed for a class of the nonstrict-feedback nonlinear systems. Compared with the existing performance functions, a new performance function, the fixed-time performance function, is proposed for the first time, which does not depend on the accurate initial value of the error signal and has the ability of finite-time convergence. Radial basis function neural network method is introduced to approximate the unknown nonlinear functions and the characteristic of the Gaussian basis functions is utilized to overcome the difficulties from the nonstrict-feedback structure. Moreover, in contrast to the traditional Backstepping technique, the command filter is introduced, which solves the “explosion of complexity” problem and relaxes the assumption on the reference signal. And then, it is guaranteed that the tracking error falls within a prescribed small neighborhood by the designed performance functions in fixed time and the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB). The effectiveness of the proposed control scheme is verified by numerical simulation. Nonstrict-Feedback Nonlinear Systems Neural Networks Adaptive Control Command Filter Prescribed Performance 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-2206193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":151852264,"identity":"251229fa-e49c-45bb-a6e6-f37902fa4d11","order_by":0,"name":"Xiaoli Yang","email":"","orcid":"","institution":"Xidian University School of Mathematics and Statistics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Yang","suffix":""},{"id":151852265,"identity":"8ede85f8-d932-4880-8ba5-1187c74e1b61","order_by":1,"name":"Jing Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACZgaDAwwMbDxA1oEDHypI08KWeHDGGeLsMYDSPMaHeVuIUX+ceeOBnzv4ZMz513w4wNvAIM8vdgC/FslmtoKDvWfYeCxnvN1wQHIHg+HM2Qn4tfAz8xgc4G1j4zG4cXbDAcMzDAkGtwloYQNqOfgXrOXMgwOJbURoAdlyGGzL+R6GAweJ0QLyy2FZsC1sBgcbzkgQ9ovB+cObP75tO2YPZDz+/KfCRp5fmoAWKDjGwCABVilBlHIQqAH66gDRqkfBKBgFo2CEAQAPNEh2ltwYBAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3668-1162","institution":"Xidian University School of Mathematics and Statistics","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":151852266,"identity":"757b5ccb-25a8-4c2c-8dc1-395f4a3ded49","order_by":2,"name":"Shuzhi Sam Ge","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuzhi","middleName":"Sam","lastName":"Ge","suffix":""},{"id":151852267,"identity":"b6afc3ce-02d1-4daa-893a-70da2df2c477","order_by":3,"name":"Xiaoling Liang","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2022-10-26 12:54:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2206193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2206193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29113014,"identity":"9cd7f7eb-a915-42d5-bd39-d8478f401bdd","added_by":"auto","created_at":"2022-11-16 03:24:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":428568,"visible":true,"origin":"","legend":"","description":"","filename":"nonstrictCF1026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2206193/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Command Filter-Based Adaptive NN Fixed-Time Prescribed Performance Control for Nonstrict-Feedback Nonlinear Systems","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":"Nonstrict-Feedback Nonlinear Systems, Neural Networks, Adaptive Control, Command Filter, Prescribed Performance","lastPublishedDoi":"10.21203/rs.3.rs-2206193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2206193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A command filter-based adaptive tracking control strategy is designed for a class of the nonstrict-feedback nonlinear systems. 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