Nonlinear Disturbance Observer Based Adaptive Neural Network Backstepping Control of Uncertain Robotic Manipulator System and Experimental Verification

Nonlinear Disturbance Observer Based Adaptive Neural Network Backstepping Control of Uncertain Robotic Manipulator System and Experimental Verification | 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 Nonlinear Disturbance Observer Based Adaptive Neural Network Backstepping Control of Uncertain Robotic Manipulator System and Experimental Verification Ke Lu, Mingjie Li, Zhicheng Zhao, Jianan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7588871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper proposes a nonlinear disturbance observer based adaptive neural network backstepping control method for uncertain robotic manipulator system. Considering the effect of robotic manipulator system with model uncertainty on the control performances, a radial basis function neural network (RBF-NN) is introduced to estimate the model uncertainties. Meanwhile, in order to reduce the influence of estimation errors and bounded external disturbances, a nonlinear disturbance observer (NDO) is used to compensate the integrated disturbances to further improve the control performances. In the control process, in order to avoid the problem of the trivial and complex calculation and improved the computational efficiency, the filtered intermediate virtual control and the estimated compensation terms are introduced into the backstepping control. Finally, the simulation and experimental results have verified the effectiveness and superiority of the proposed method. Uncertain robotic manipulator system Radial basis function neural network (RBF-NN) Nonlinear disturbance observer (NDO) Backstepping control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Sep, 2025 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 11 Sep, 2025 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. 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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-7588871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521618083,"identity":"afb7e0a1-c0b3-4696-a020-4d0fedc18f8b","order_by":0,"name":"Ke Lu","email":"","orcid":"","institution":"Taiyuan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Lu","suffix":""},{"id":521618084,"identity":"45adf633-c70e-4d23-a224-fde516cfd0de","order_by":1,"name":"Mingjie 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Considering the effect of robotic manipulator system with model uncertainty on the control performances, a radial basis function neural network (RBF-NN) is introduced to estimate the model uncertainties. Meanwhile, in order to reduce the influence of estimation errors and bounded external disturbances, a nonlinear disturbance observer (NDO) is used to compensate the integrated disturbances to further improve the control performances. In the control process, in order to avoid the problem of the trivial and complex calculation and improved the computational efficiency, the filtered intermediate virtual control and the estimated compensation terms are introduced into the backstepping control. Finally, the simulation and experimental results have verified the effectiveness and superiority of the proposed method.\u003c/p\u003e","manuscriptTitle":"Nonlinear Disturbance Observer Based Adaptive Neural Network Backstepping Control of Uncertain Robotic Manipulator System and Experimental Verification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 13:13:27","doi":"10.21203/rs.3.rs-7588871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-17T21:02:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T18:11:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T00:47:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent \u0026 Robotic Systems","date":"2025-09-11T07:11:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71987a33-d4a1-4a2e-b896-de932499413a","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T13:13:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 13:13:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7588871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7588871","identity":"rs-7588871","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}