Predefined-Time Sliding Mode Control for Position and Attitude of Quadrotor with Variable Exponent Coefficients

Predefined-Time Sliding Mode Control for Position and Attitude of Quadrotor with Variable Exponent Coefficients | 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 Predefined-Time Sliding Mode Control for Position and Attitude of Quadrotor with Variable Exponent Coefficients Lifeng Hou, Jianhua Zhang, Zhanyang Yu, Xinxin Wang, Cheng Siong Chin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7153474/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract This paper presents a comprehensive study on predefined-time sliding mode control applied to quadrotor systems with variable exponent coefficients. The main goal is to achieve efficient control with predefined-time convergence to the desired target. Novel predefined-time sliding mode control and predefined-time neural networks sliding model control algorithms are developed to adapt to the dynamics of variable coefficients and ensure system stability. The stability analysis confirms their capability to achieve predefined-time convergence. Extensive numerical simulations on diverse nonlinear systems validate the superiority of the proposed approach over conventional sliding mode control and neural networks control. Physical sciences/Engineering Physical sciences/Mathematics and computing Predefined-time stability sliding mode control neural networks trajectory tracking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Editor invited by journal 01 Aug, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 30 Jul, 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. 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