K–R Adaptive Flight Control: Physics-Informed Nonlinear Residual Correction for Robust Trajectory Tracking Under Model Uncertainty

K–R Adaptive Flight Control: Physics-Informed Nonlinear Residual Correction for Robust Trajectory Tracking Under Model Uncertainty | 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 K–R Adaptive Flight Control: Physics-Informed Nonlinear Residual Correction for Robust Trajectory Tracking Under Model Uncertainty RamaKrishna Pasupuleti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9263339/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 paper presents a physics-informed adaptive control framework, termed K–R control, that combines a linear model-based controller (K-step) with an online nonlinear residual correction (R-step) for robust flight trajectory tracking. The R-step employs recursive least squares with ridge regularization to learn a nonlinear feature-based correction that compensates for model mismatch, aerodynamic nonlinearities, and external disturbances without requiring offline training data. The method is validated on a nonlinear F-16 aircraft model using Stevens & Lewis aerodynamic lookup tables and MIL-F-8785C Dryden turbulence. Seven scenarios are tested including extreme turbulence with 40% mass mismatch, near-stall flight, sensor failure, parameter drift, adversarial disturbances, computational latency, and multi-axis coupled control. K–R is compared against PID, LQR, MPC, L1 adaptive, and ablated variants across 100-run Monte Carlo campaigns with statistical significance testing. Results show K–R achieves the lowest RMSE in 5 of 7 scenarios, with 73% improvement over LQR under parameter drift and 42% improvement in disturbance estimation quality, while maintaining bounded weights and real-time feasibility at 34 µs per update. Aeronautics and Astronautics Adaptive Flight Control Nonlinear Control Physics-Informed Learning Residual Correction Recursive Least Squares Full Text Additional Declarations The authors declare no competing interests. Supplementary Files controllers.py Controllers Code coretests.py Coretests Code f16model.py F16 model fastsim.py Fast sim code finalchecks.py Final check code generatefigures.py Genarate figures code theorysuite.py Theory suite code turbulence.py Turbulence code KRFlightControlSupplementaryMaterial.docx Supplementary material 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. 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-9263339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614332061,"identity":"deea9387-cb0c-4a69-98bf-cfbcd208dde3","order_by":0,"name":"RamaKrishna 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Flight Control; Nonlinear Control; Physics-Informed Learning; Residual Correction; Recursive Least Squares","lastPublishedDoi":"10.21203/rs.3.rs-9263339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9263339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a physics-informed adaptive control framework, termed K\u0026ndash;R control, that combines a linear model-based controller (K-step) with an online nonlinear residual correction (R-step) for robust flight trajectory tracking. The R-step employs recursive least squares with ridge regularization to learn a nonlinear feature-based correction that compensates for model mismatch, aerodynamic nonlinearities, and external disturbances without requiring offline training data. The method is validated on a nonlinear F-16 aircraft model using Stevens \u0026amp; Lewis aerodynamic lookup tables and MIL-F-8785C Dryden turbulence. Seven scenarios are tested including extreme turbulence with 40% mass mismatch, near-stall flight, sensor failure, parameter drift, adversarial disturbances, computational latency, and multi-axis coupled control. K\u0026ndash;R is compared against PID, LQR, MPC, L1 adaptive, and ablated variants across 100-run Monte Carlo campaigns with statistical significance testing. 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