Robust Quadrotor Position Tracking via Fuzzy Adaptive with Outer-Loop Sliding Mode Control

Robust Quadrotor Position Tracking via Fuzzy Adaptive with Outer-Loop Sliding Mode Control | 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 Robust Quadrotor Position Tracking via Fuzzy Adaptive with Outer-Loop Sliding Mode Control Sotirios Spanogianopoulos, Mostafa Jalalnezhad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8394499/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract The present research introduces an integrated and computationally efficient control and optimization framework aimed at enhancing the robustness, adaptability, and real-time efficiency of unmanned aerial vehicle (UAV) operations in complex, uncertain, and dynamically changing environments. The proposed hybrid system combines an Adaptive Fuzzy Feedback Linearization Controller (AFFLC) with an outer-loop Sliding-Mode Controller (SMC) and a Late Acceptance Hill-Climbing (LAHC) path-planning optimizer, building a two-layer intelligent structure capable of handling both nonlinear flight dynamics and global trajectory optimization. The innovation of the research lies in the formulation of a fuzzy adaptive law that modulates internal control gains according to instantaneous tracking error and its derivative, ensuring smooth and rapid responses while effectively mitigating chattering without relying on disturbance observers or heavy parameter tuning. The importance of this study stems from the growing demand for lightweight, low-complexity algorithms that can sustain stable performance under severe operational conditions—such as payload uncertainty, wind disturbance, and limited onboard computational power—where conventional control and meta-heuristic methods often fail. The objectives include designing a dual-loop AFFLC–SMC system for robust stabilization, deriving Lyapunov-based guarantees of global boundedness and convergence, and integrating a LAHC-based global route optimizer minimizing the combined cost of distance, energy consumption, and threat exposure. Quantitative and qualitative analyses confirm that the proposed approach achieves the smallest tracking error, negligible steady-state deviation, and fastest dynamic recovery. Specifically, the AFFLC suppresses rotor chattering by over 70% and reduces total convergence time by ≈ 35% compared with conventional adaptive and sliding-mode controllers, while the LAHC algorithm yields the lowest path cost and execution time across all benchmark scenarios, stabilizing within ≈ 100 iterations—far faster than GWO, SOS, SA, and HGWO-MSOS optimizers. The associated tables and figures demonstrate that the adaptive-gain surface produces nonlinear stabilization behavior, effectively balancing transient acceleration and steady-state smoothness, while the 3D simulation results highlight the planner’s capability in maintaining safe, energy-efficient navigation even in densely constrained, multi-obstacle environments. Collectively, the integrated AFFLC–SMC + LAHC framework offers a lightweight, high-precision, and computationally scalable solution readily applicable to real-time UAV flight missions, emphasizing a practical step toward intelligent autonomous aerial systems for industrial, security, and environmental applications. UAV Deep reinforcement learning Robotic control CoppeliaSim Deep deterministic policy gradient Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 11 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 18 Dec, 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. We do this by developing innovative software and high quality services for the global research community. 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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-8394499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573652851,"identity":"b2fa5056-09e4-4472-8b4c-2c9ffc5b00ff","order_by":0,"name":"Sotirios 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The proposed hybrid system combines an Adaptive Fuzzy Feedback Linearization Controller (AFFLC) with an outer-loop Sliding-Mode Controller (SMC) and a Late Acceptance Hill-Climbing (LAHC) path-planning optimizer, building a two-layer intelligent structure capable of handling both nonlinear flight dynamics and global trajectory optimization. The \u003cb\u003einnovation\u003c/b\u003e of the research lies in the formulation of a fuzzy adaptive law that modulates internal control gains according to instantaneous tracking error and its derivative, ensuring smooth and rapid responses while effectively mitigating chattering without relying on disturbance observers or heavy parameter tuning. The importance of this study stems from the growing demand for lightweight, low-complexity algorithms that can sustain stable performance under severe operational conditions\u0026mdash;such as payload uncertainty, wind disturbance, and limited onboard computational power\u0026mdash;where conventional control and meta-heuristic methods often fail. The objectives include designing a dual-loop AFFLC\u0026ndash;SMC system for robust stabilization, deriving Lyapunov-based guarantees of global boundedness and convergence, and integrating a LAHC-based global route optimizer minimizing the combined cost of distance, energy consumption, and threat exposure. Quantitative and qualitative analyses confirm that the proposed approach achieves the smallest tracking error, negligible steady-state deviation, and fastest dynamic recovery. Specifically, the AFFLC suppresses rotor chattering by over 70% and reduces total convergence time by \u0026asymp;\u0026thinsp;35% compared with conventional adaptive and sliding-mode controllers, while the LAHC algorithm yields the lowest path cost and execution time across all benchmark scenarios, stabilizing within \u0026asymp;\u0026thinsp;100 iterations\u0026mdash;far faster than GWO, SOS, SA, and HGWO-MSOS optimizers. The associated tables and figures demonstrate that the adaptive-gain surface produces nonlinear stabilization behavior, effectively balancing transient acceleration and steady-state smoothness, while the 3D simulation results highlight the planner\u0026rsquo;s capability in maintaining safe, energy-efficient navigation even in densely constrained, multi-obstacle environments. Collectively, the integrated AFFLC\u0026ndash;SMC\u0026thinsp;+\u0026thinsp;LAHC framework offers a lightweight, high-precision, and computationally scalable solution readily applicable to real-time UAV flight missions, emphasizing a practical step toward intelligent autonomous aerial systems for industrial, security, and environmental applications.\u003c/p\u003e","manuscriptTitle":"Robust Quadrotor Position Tracking via Fuzzy Adaptive with Outer-Loop Sliding Mode Control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 03:39:47","doi":"10.21203/rs.3.rs-8394499/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T17:47:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T06:46:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T06:16:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235445391705089250064436123782252399871","date":"2026-02-02T14:12:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73334687165317628373497937201023500403","date":"2026-01-12T13:58:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T04:22:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T23:59:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T23:58:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent \u0026 Robotic Systems","date":"2025-12-18T10:40:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"573330f6-7391-47da-a59a-1d3fb75fb8a2","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T17:54:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 03:39:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8394499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8394499","identity":"rs-8394499","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}