Adaptive Super Twisting Sliding Mode Position Control for Series Elastic Actuator Robot using Radial Basis Function Neural Network

Adaptive Super Twisting Sliding Mode Position Control for Series Elastic Actuator Robot using Radial Basis Function 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 Research Article Adaptive Super Twisting Sliding Mode Position Control for Series Elastic Actuator Robot using Radial Basis Function Neural Network Thi Ly Tong, Ngoc Quy Nguyen, Thanh Tung Tran, Minh Duc Duong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7705533/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 Series Elastic Actuators (SEA) provide improved control, safety, energy efficiency, and performance compared to traditional rigid actuators, making them well-suited for various applications in robotics, rehabilitation, and human-robot interaction. However, the inherent flexibility of SEAs can lead to oscillations in the position of SEA robots. This paper introduces a radial basis function (RBF) neural network-based adaptive super-twisting sliding mode control approach for position tracking of SEA robots. The robust control part of the proposed strategy, the super-twisting sliding mode control, effectively provides stability and robustness, demonstrates finite-time convergence, and suppresses oscillations caused by the joints. Beyond implementing robust control, the Radial Basis Function (RBF) neural network coordination is crucial for effectively approximating the unknown components of the manipulator dynamical model and uncertainties encountered in practical applications. The unknown nonlinearities are approximated through an RBF neural network, wherein the network's weight parameters are dynamically adjusted in real-time based on adaptive laws. Leveraging the RBF model, an adaptive control algorithm is formulated via the Lyapunov synthesis approach. Simulation outcomes corroborate the efficacy of the proposed controller in attaining accurate position tracking while effectively reducing oscillations. Mathematics Subject Classification (2020) 93C10 · 93C40 · 93B52 ·70Q05 Series Elastic Actuator Flexible Robot Super Twisting Sliding mode Control Radial Basis Function Neural Network Adaptive Control 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. 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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-7705533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528013750,"identity":"daabb35a-ca14-4e79-b1cd-86dda84c1cd9","order_by":0,"name":"Thi Ly Tong","email":"","orcid":"","institution":"Hanoi University of Industry","correspondingAuthor":false,"prefix":"","firstName":"Thi","middleName":"Ly","lastName":"Tong","suffix":""},{"id":528013751,"identity":"4ae578af-e0aa-42c9-bf62-f063f4c0a7db","order_by":1,"name":"Ngoc Quy Nguyen","email":"","orcid":"","institution":"Hanoi University of Science and 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However, the inherent flexibility of SEAs can lead to oscillations in the position of SEA robots. This paper introduces a radial basis function (RBF) neural network-based adaptive super-twisting sliding mode control approach for position tracking of SEA robots. The robust control part of the proposed strategy, the super-twisting sliding mode control, effectively provides stability and robustness, demonstrates finite-time convergence, and suppresses oscillations caused by the joints. Beyond implementing robust control, the Radial Basis Function (RBF) neural network coordination is crucial for effectively approximating the unknown components of the manipulator dynamical model and uncertainties encountered in practical applications. The unknown nonlinearities are approximated through an RBF neural network, wherein the network's weight parameters are dynamically adjusted in real-time based on adaptive laws. 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