Practical Insights On Data-Based Robot Control: A Comparative Analysis of Data-Enabled Predictive Control and Model-Based Predictive Control

Practical Insights On Data-Based Robot Control: A Comparative Analysis of Data-Enabled Predictive Control and Model-Based Predictive 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 Practical Insights On Data-Based Robot Control: A Comparative Analysis of Data-Enabled Predictive Control and Model-Based Predictive Control Jingshan Chen, Henrik Ebel, Peter Eberhard This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7571235/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract When partial knowledge of a system is already available, is it reasonable to disregard this information in favor of a purely data-driven control approach? In this study, Data-Enabled Predictive Control (DeePC) is implemented on a custom-built omnidirectional mobile robot and a comparative evaluation against Model Predictive Control (MPC) is conducted seeking to address this question. To ensure a fair comparison, a linear model used for MPC is identified from the same dataset employed by DeePC. Additionally, a supplementary comparison using a nonlinear nominal model is provided. All results are derived from hardware experiments, which are essential for assessing the real-world applicability of DeePC, particularly in robotics, where system dynamics is often nonlinear and data is constantly interfered with by noise. The findings offer practical insights into the challenges and strengths of DeePC, which has been claimed to be robust to nonlinearity in the literature, aiding practitioners in selecting the most suitable predictive control strategy for their specific purposes. data-enabled predictive control model predictive control system identification robot control hardware implementation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 17 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 09 Sep, 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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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-7571235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518683706,"identity":"1b99fcb1-76d7-4f40-9faa-ac8fa7268151","order_by":0,"name":"Jingshan Chen","email":"","orcid":"","institution":"University of Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Jingshan","middleName":"","lastName":"Chen","suffix":""},{"id":518683707,"identity":"961be2ad-795c-499e-86e6-ad17a068470c","order_by":1,"name":"Henrik Ebel","email":"","orcid":"","institution":"Lappeenranta University of 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