Observed-based adaptive fuzzy control for robotic manipulators  with output and input  quantization

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Abstract The most existing control studies of robotic manipulators are restricted to nonlinear systems without quantized signals. To eliminate this restriction, for a class of robotic systems with output and input quantization, a novel observer-based adaptive fuzzy control scheme is raised in this paper. In view of the quantized output and input signals, a novel quantized state observer with n-dimensional states is devised to evaluate the unmeasurable states. In the backstepping process, the incorporation of quantized output signals leads to discontinuity in the virtual controllers. To avoid this problem, the solution is divided into three steps: First of all, auxiliary intermediate controllers are designed using the command-filtered backstepping technique. Secondly, by substituting quantized states for unquantized ones in the auxiliary intermediate controllers, the actual torque controller is derived. Thirdly, Lemma 5 is presented to deal with the effects of quantization errors. Furthermore, the stability of robot systems with n-dimensional states can be ensured.
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Observed-based adaptive fuzzy control for robotic manipulators with output and input quantization | 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 Article Observed-based adaptive fuzzy control for robotic manipulators with output and input quantization Zhuoxing Du, Hongyu Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7495170/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The most existing control studies of robotic manipulators are restricted to nonlinear systems without quantized signals. To eliminate this restriction, for a class of robotic systems with output and input quantization, a novel observer-based adaptive fuzzy control scheme is raised in this paper. In view of the quantized output and input signals, a novel quantized state observer with n-dimensional states is devised to evaluate the unmeasurable states. In the backstepping process, the incorporation of quantized output signals leads to discontinuity in the virtual controllers. To avoid this problem, the solution is divided into three steps: First of all, auxiliary intermediate controllers are designed using the command-filtered backstepping technique. Secondly, by substituting quantized states for unquantized ones in the auxiliary intermediate controllers, the actual torque controller is derived. Thirdly, Lemma 5 is presented to deal with the effects of quantization errors. Furthermore, the stability of robot systems with n-dimensional states can be ensured. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics robotic manipulator adaptive control backstepping input and output quantization fuzzy control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Nov, 2025 Reviews received at journal 13 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 26 Sep, 2025 Editor invited by journal 24 Sep, 2025 Submission checks completed at journal 23 Sep, 2025 First submitted to journal 23 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. 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