Design and Implementation of Deep Learning-Driven Sensorless Controller for DC LED Street Lights

Design and Implementation of Deep Learning-Driven Sensorless Controller for DC LED Street Lights | 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 Design and Implementation of Deep Learning-Driven Sensorless Controller for DC LED Street Lights Krishna Perumallapalli, Devendra Potnuru This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490831/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The adoption of energy-efficient and intelligent control systems is critical for modern LED-based street lighting solutions. Traditional control methods, while effective, often struggle to adapt to non-linearities and dynamic changes in system parameters. This paper presents the design and implementation of a novel sensorless controller for boost converter driven DC LED Street lights, leveraging deep learning to achieve accurate and robust voltage regulation without a physical voltage sensor. A Deep Neural Network (DNN) is trained to estimate the output voltage of the system using measurable parameters such as input voltage, load current, and duty cycle. This estimated voltage is then utilized in a control loop to maintain stable operation under varying load and environmental conditions. The DNN model is trained on simulated and experimental data representing a wide range of operating conditions, including load variations. The controller is implemented on DSP controller platform and validated on a laboratory prototype of 500W DC boost converter-based LED driver. Performance evaluation is demonstrated using simulation and experimental results. This work provides a scalable and cost-effective solution for modern street lighting systems, paving the way for smarter and more sustainable urban lighting infrastructure. DC-DC converter Boost LED driver Street light Deep-Learning Sensorless control Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 12 Mar, 2026 Editor invited by journal 02 Feb, 2026 Editor assigned by journal 02 Jan, 2026 First submitted to journal 02 Jan, 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. 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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-8490831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605179155,"identity":"89b3c29d-3974-43cf-9bd9-1de5135a81d3","order_by":0,"name":"Krishna Perumallapalli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACHgaGA4wNYCbjAxCfjxQtzAYgPhsxWhigWtgkwCQhHeY8Zx8e/Lnjjjw/e/Oxyq85djJsDMwPH93Ao8Wyt93gMO+ZZ4Yze46l3Zbdlgx0GJuxcQ4eLQbn2RgOM7YdZtxwI8fstuQ2ZqAWHjZpQloO/mw7bL///vtvxZLb6onQcraN4QBv2+HEDRI8bIwftx0mrMWy5xjDYaCW5Bln0oylGbcd52FjJuAXc5405o9Ah9n2tx9++PHntmp7YNA9fIzXYcgcZh4wiUc5hhbGHwRUj4JRMApGwcgEABzeSkABjbmaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-6876-4117","institution":"Rajiv Gandhi University of Knowledge Technologies","correspondingAuthor":true,"prefix":"","firstName":"Krishna","middleName":"","lastName":"Perumallapalli","suffix":""},{"id":605179156,"identity":"11e1245f-550d-4496-b130-bf2a27d2b442","order_by":1,"name":"Devendra Potnuru","email":"","orcid":"","institution":"North Eastern Regional Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Devendra","middleName":"","lastName":"Potnuru","suffix":""}],"badges":[],"createdAt":"2025-12-31 15:25:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8490831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8490831/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104783050,"identity":"7640ffd0-54c4-4ac1-a183-0c4844634d8b","added_by":"auto","created_at":"2026-03-17 07:58:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702904,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerLEDdriverpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8490831/v1_covered_c1c9e627-3fc2-478a-8457-493b2cdc8cfd.pdf"}],"financialInterests":"","formattedTitle":"Design and Implementation of Deep Learning-Driven Sensorless Controller for DC LED Street Lights","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"DC-DC converter, Boost LED driver, Street light, Deep-Learning, Sensorless control","lastPublishedDoi":"10.21203/rs.3.rs-8490831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8490831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe adoption of energy-efficient and intelligent control systems is critical for modern LED-based street lighting solutions. Traditional control methods, while effective, often struggle to adapt to non-linearities and dynamic changes in system parameters. This paper presents the design and implementation of a novel sensorless controller for boost converter driven DC LED Street lights, leveraging deep learning to achieve accurate and robust voltage regulation without a physical voltage sensor. A Deep Neural Network (DNN) is trained to estimate the output voltage of the system using measurable parameters such as input voltage, load current, and duty cycle. This estimated voltage is then utilized in a control loop to maintain stable operation under varying load and environmental conditions. The DNN model is trained on simulated and experimental data representing a wide range of operating conditions, including load variations. The controller is implemented on DSP controller platform and validated on a laboratory prototype of 500W DC boost converter-based LED driver. 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