H∞ Robust Sampled-Data State-Feedback Control of Linear 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 Research Article H ∞ Robust Sampled-Data State-Feedback Control of Linear Systems Rafael Martins Alves, André Ricardo Fioravanti, Matheus Souza This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6356264/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Journal of Control, Automation and Electrical Systems → Version 1 posted 11 You are reading this latest preprint version Abstract In this paper, we devise sampled-data state-feedback controllers for uncertain linear systems that ensure robust stability and H∞ performance. We model the closed-loop system as a hybrid linear dynamic system with polytopic or interval uncertainties, capturing continuous and discrete-time behaviours in a unified framework. This approach avoids the proliferation of uncertain parameters typically encountered in discretization methods. Using this hybrid model, we derive computationally tractable control design conditions that guarantee an upper bound for the associated closed-loop $\Hoo$ norm. Our formulation enables designers to select between interval methods that offer computational efficiency for systems with numerous independent uncertainties, or polytopic methods that provide less conservative results at higher computational cost. Numerical examples with detailed simulation verification demonstrate the effectiveness of our proposed techniques and validate the theoretical performance guarantees across the entire uncertainty space. H control Robust control Hybrid systems Sampled-data systems Interval uncertainty Polytopic uncertainty Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Journal of Control, Automation and Electrical Systems → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 06 May, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviews received at journal 08 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Editor assigned by journal 05 Apr, 2025 Submission checks completed at journal 05 Apr, 2025 First submitted to journal 01 Apr, 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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