RBF Network Dynamic Sliding Mode Robust Control for Overhead Cranes with Uncertain Parameters

RBF Network Dynamic Sliding Mode Robust Control for Overhead Cranes with Uncertain Parameters | 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 RBF Network Dynamic Sliding Mode Robust Control for Overhead Cranes with Uncertain Parameters shihua li, YouShan Gao, Shi-Ning Lv, Ai-Hong Wang, Yu-Fan Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3060919/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jun, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract For the load swing and trolley tracking and positioning issues of overhead crane system during lifting operations with uncertain inherent parameters and partial unknown parameters, a dynamic sliding mode robust control algorithm based on RBF neural network is suggested. The Lyapunov stability of the closed loop error is Theoretically testified by the control algorithm .The control algorithm takes the sliding mode control idea as its structural framework. The design of the sliding mode surface takes into account the impact of the alteration in the control output, makes the sliding mode switching term adaptive by setting fuzzy rules ,and further derives the self adaption laws of the RBF neural networks to adapt the intricated as well as unknown dynamic parts in the system of overhead crane, rendering the operation of the system of overhead crane with no need for any system parameters as the signal input. For the sake of testifying the superior control accuracy under the influence of this tactics, a comparison was made with the layered sliding mode control method, with results demonstrated that the output power of the controller based on above algorithm designed in this paper proves more stable, and there is no long-term high-frequency jitter, while its anti-swing control effect on the load is also standing a better robust performance of any other controllers. It is also exceedingly significant to point out that when the system parameters change to some extent. While the trolley based on this control system can also achieve stable tracking and positioning under zero overshoot. Overhead crane Anti-swing RBF network Track planning Lyapunov theorem Dynamic sliding mode Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Jun, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted Reviewers agreed at journal 08 Mar, 2024 Reviewers invited by journal 08 Mar, 2024 Editor assigned by journal 20 Jun, 2023 First submitted to journal 14 Jun, 2023 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-3060919","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277510863,"identity":"aa095db9-633f-4b61-8af6-044096ba4fc5","order_by":0,"name":"shihua 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