Human-Simulated Intelligent Control of Torsional Vibration in Magneto-rheological Variable Stiffness-Damping-Moment of Inertia Transmission System based on Hybrid Taguchi Genetic Algorithm

Human-Simulated Intelligent Control of Torsional Vibration in Magneto-rheological Variable Stiffness-Damping-Moment of Inertia Transmission System based on Hybrid Taguchi Genetic Algorithm | 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 Human-Simulated Intelligent Control of Torsional Vibration in Magneto-rheological Variable Stiffness-Damping-Moment of Inertia Transmission System based on Hybrid Taguchi Genetic Algorithm Wenfeng Li, Le Liu, Xinfeng Ge, Manying Liang, Xiong Deng, Jianrong Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4538853/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jun, 2025 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract In addressing the distinct influence mechanisms of torsional stiffness, torsional damping, and moment of inertia on the torsional vibration behavior of transmission systems, a human-simulated intelligent control strategy based on hybrid Taguchi genetic algorithm is proposed for the torsional vibration of magneto-rheological (MR) transmission system with variable stiffness, variable damping and variable inertia. Initially, the research conducts an in-depth analysis of the effects exerted by torsional stiffness, torsional damping, and moment of inertia on the transmission system dynamics. Subsequently, a comprehensive dynamics model for the MR transmission system with adaptable stiffness, damping, and inertia parameters is formulated, grounded on the conceptualization of an MR torsional vibration absorber. Drawing from the dynamic optimization outcomes facilitated by the hybrid Taguchi genetic algorithm, control parameters governing torsional stiffness, torsional damping, and moment of inertia are refined. This refinement process underpins the design of a human-simulated intelligent controller distinguished by its partitioned and multimodal characteristics. Notably, this controller framework is crafted to accommodate considerations across both temporal and spectral domains. Following the controller's design phase, rigorous simulation analyses are conducted to assess its efficacy. Results substantiate that the human-simulated intelligent control mechanism, underpinned by the hybrid Taguchi genetic algorithm, proficiently attenuates torsional vibrations across a broad spectrum of frequency bands. Furthermore, it is observed to markedly enhance the overall output characteristics of the MR transmission system. magneto-rheological variable stiffness variable damping and variable inertia hybrid Taguchi genetic algorithm human-simulated intelligent control Full Text Cite Share Download PDF Status: Published Journal Publication published 27 Jun, 2025 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted Reviewers agreed at journal 19 Aug, 2024 Reviewers invited by journal 22 Jun, 2024 Editor assigned by journal 10 Jun, 2024 First submitted to journal 06 Jun, 2024 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-4538853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317762144,"identity":"88c41d03-0816-4153-88c9-eb47ab940daa","order_by":0,"name":"Wenfeng 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Results substantiate that the human-simulated intelligent control mechanism, underpinned by the hybrid Taguchi genetic algorithm, proficiently attenuates torsional vibrations across a broad spectrum of frequency bands. 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