A New Self-Adaptive Fuzzy PID Tracking Control for Autonomous Wheelchairs

A New Self-Adaptive Fuzzy PID Tracking Control for Autonomous Wheelchairs | 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 New Self-Adaptive Fuzzy PID Tracking Control for Autonomous Wheelchairs Mohamed A. Shamseldin, Mohamed Ismail This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9158708/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Autonomous wheelchairs require robust control strategies to ensure accurate and safe navigation under system uncertainties and environmental disturbances. This paper presents a systematic design framework for trajectory tracking control based on a neuro-fuzzy identified model of an experimental wheelchair platform. Real input–output data were collected by exciting the system with a pseudo-random binary sequence (PRBS) signal, and the corresponding linear and angular velocities were recorded. A nonlinear model was then developed using an adaptive neuro-fuzzy inference system and validated against experimental measurements. Based on the identified model, a self-adaptive fuzzy PID (SAFPID) controller was designed to improve tracking performance and suppress vibration around the reference path. The controller employs an online adaptation mechanism to tune the membership function parameters, while the main control gains were optimized using the COVID-19 optimization algorithm through a multi-objective performance index. The proposed approach was implemented on an embedded platform consisting of an Arduino Mega 2560 and a Raspberry Pi 3. Experimental results demonstrate that the proposed controller significantly outperforms a conventional PID controller, reducing the root mean square tracking error from 1.258 m to 0.598 m and from 5.359 m to 1.365 m for two different trajectories. Physical sciences/Engineering Physical sciences/Mathematics and computing adaptive control autonomous wheelchair neuro-fuzzy modeling self-adaptive fuzzy PID system identification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 30 Mar, 2026 Editor invited by journal 30 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 25 Mar, 2026 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-9158708","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614981102,"identity":"0ee2ef77-e8cc-459e-8988-0515085b1e14","order_by":0,"name":"Mohamed A. 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