A BLMI-Based Framework for Robust Stabilization of Stochastic Hybrid Systems Under Dual-Mode Uncertainties

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This paper presents a BLMI-based framework for robust state-feedback control design in stochastic hybrid systems with parametric and Markovian transition rate uncertainties, ensuring exponential mean-square stability.

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This paper studies robust stabilization for linear stochastic hybrid systems with two kinds of uncertainty: parametric changes in system matrices and bounded uncertainty in Markovian switching transition rates. Using bilinear matrix inequalities (BLMIs), the authors derive novel sufficient conditions for exponential mean-square stability and propose a systematic robust state-feedback controller design that does not require exact transition-rate knowledge. Numerical simulations are used to confirm the method’s effectiveness in cases with coexisting parametric and structural uncertainties. The paper is a preprint and explicitly has not been peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract This study investigates the robust stabilization problem for linear stochastic hybrid systems (SHSs) characterized by dual uncertainties: parametric variations in system matrices and bounded uncertainties in Markovian transition rates. Novel sufficient conditions for exponential mean-square stability are derived via bilinear matrix inequalities (BLMIs). A systematic methodology is proposed to design robust state-feedback controllers, eliminating reliance on exact transition rate knowledge. Numerical simulations confirm the frameworks efficacy, demonstrating its applicability in scenarios with coexisting parametric and structural uncertainties.
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A BLMI-Based Framework for Robust Stabilization of Stochastic Hybrid Systems Under Dual-Mode Uncertainties | 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 A BLMI-Based Framework for Robust Stabilization of Stochastic Hybrid Systems Under Dual-Mode Uncertainties Tao Wang, Fubo Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6257186/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 This study investigates the robust stabilization problem for linear stochastic hybrid systems (SHSs) characterized by dual uncertainties: parametric variations in system matrices and bounded uncertainties in Markovian transition rates. Novel sufficient conditions for exponential mean-square stability are derived via bilinear matrix inequalities (BLMIs). A systematic methodology is proposed to design robust state-feedback controllers, eliminating reliance on exact transition rate knowledge. Numerical simulations confirm the frameworks efficacy, demonstrating its applicability in scenarios with coexisting parametric and structural uncertainties. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Applied mathematics Bilinear matrix inequalities (BLMIs) Robust stabilization Stochastic hybrid sys- tems (SHSs) Uncertain transition rates Markovian switching 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. 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. 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