Robust control of multi joint robotic arm visual servoing based on neural network and adaptive instruction filtering

Robust control of multi joint robotic arm visual servoing based on neural network and adaptive instruction filtering | 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 Robust control of multi joint robotic arm visual servoing based on neural network and adaptive instruction filtering Kesen Jiang, Junqing Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9237317/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This paper proposes a multi joint robotic arm visual servo robust control strategy based on neural networks and adaptive instruction filtering, aiming to solve dynamic uncertainty and nonlinear problems. By constructing an image plane feature point error model and deriving the image Jacobian matrix, a mapping relationship between image velocity and joint velocity was established. Using radial basis function neural network (RBFNN) to approximate the uncertainty of the dynamic model of the robotic arm online, the dynamic model error is reduced by 15% -20%. Designed an adaptive gain instruction filter, reducing filtering error by about 20% and computational complexity by 10% -15%. Experiments have shown that the tracking error (RMSE) is reduced by 15% -25%, and the tracking success rate is increased to over 90%. The anti saturation mechanism effectively ensures system stability under actuator constraints. Visual servoing control Radial basis function neural network Adaptive instruction filtering Robust control Multi joint robotic arm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 29 Mar, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 26 Mar, 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. 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-9237317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618358800,"identity":"8a035b95-cc73-4466-af94-eff29dfb5110","order_by":0,"name":"Kesen Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACgwMg0kYCSLAffJBQUUNYi2EDiOQBaeFJNnhw5hhhLcZgkgdMmkk+bGEmrMWMvf3h4wIeC3lz/gVpFYkNbAz87d0JeLXY8JwxNp7BI2G4c8bDYzcSd8gwSJw5uwG/FokcNmkeHgnGDTcOpN1IPMPGYCCRi1+Lmfzz57+BWuyBWswKEtuYCWsxlmAwY+bpkUjccL7BjIEoLYY9OcbSPDckkjfc4EmWSDhzjIegXwyOH3/4maegznbD+eMHP/6oqJHjb+/FrwUBJBLAFA+RykGA/wAJikfBKBgFo2BEAQA9Xki5rVmTtgAAAABJRU5ErkJggg==","orcid":"","institution":"CHINA TOBACCO HENAN INDUSTRIAL CO.,LTD","correspondingAuthor":true,"prefix":"","firstName":"Kesen","middleName":"","lastName":"Jiang","suffix":""},{"id":618358801,"identity":"1e07b990-e287-4a9d-9892-afedc023b046","order_by":1,"name":"Junqing Yan","email":"","orcid":"","institution":"CHINA TOBACCO HENAN INDUSTRIAL CO.,LTD","correspondingAuthor":false,"prefix":"","firstName":"Junqing","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2026-03-26 18:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9237317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9237317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106726571,"identity":"2d049acb-3553-467c-a112-3feca4f90173","added_by":"auto","created_at":"2026-04-12 18:36:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281666,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9237317/v1_covered_6972d6db-9b7e-46fe-b930-3bb97f18b641.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust control of multi joint robotic arm visual servoing based on neural network and adaptive instruction filtering","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Visual servoing control, Radial basis function neural network, Adaptive instruction filtering, Robust control, Multi joint robotic arm","lastPublishedDoi":"10.21203/rs.3.rs-9237317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9237317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a multi joint robotic arm visual servo robust control strategy based on neural networks and adaptive instruction filtering, aiming to solve dynamic uncertainty and nonlinear problems. 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