Integrated Learning Based Control Framework for Stabilizing Discrete-Time Dynamical Systems

Integrated Learning Based Control Framework for Stabilizing Discrete-Time Dynamical Systems | 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 Integrated Learning Based Control Framework for Stabilizing Discrete-Time Dynamical Systems E. Maftoolkar, A. H. Mazinan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8793678/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 22 You are reading this latest preprint version Abstract This paper investigates the stabilization problem of discrete-time linear and nonlinear systems under uncertainties and external disturbances. A dynamic Lyapunov-based control framework is proposed to improve stability and convergence performance. The framework incorporates data-driven adaptation to address unknown or time-varying system dynamics and employs parallel computational structures to enable efficient real-time stability evaluation. The proposed method enhances robustness and convergence speed without imposing restrictive assumptions on system models. Numerical studies under stochastic perturbations, parameter variations, and boundary operating conditions demonstrate up to a 50% improvement in convergence speed and a 25% enhancement in stability prediction accuracy compared to conventional approaches. Further validation through large-scale simulations and hardware-in-the-loop experiments on aerospace benchmark systems confirms the effectiveness and practical applicability of the proposed framework. Physical sciences/Engineering Physical sciences/Mathematics and computing Discrete-time linear and nonlinear systems Lyapunov stability Machine learning Parallel computing Hybrid control Intelligent sensing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Editor invited by journal 10 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 09 Feb, 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-8793678","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591521828,"identity":"e5e5be44-dfee-43cc-a3f0-599d0f424aa7","order_by":0,"name":"E. 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