Research on Trajectory Tracking of Unmanned Excavators Based on Nonlinear Model Predictive Control

Research on Trajectory Tracking of Unmanned Excavators Based on Nonlinear Model 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 Research on Trajectory Tracking of Unmanned Excavators Based on Nonlinear Model Predictive Control Jiwen Liu, Yaxin Wu, Huan Yang, Di Hu, Ran Zhang, Jintao Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7725337/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 The hydraulic excavator is a three-degree-of-freedom robotic arm consisting of the boom, arm, and bucket. To address the significant time delays and nonlinearities inherent in hydraulic excavators and achieve precise trajectory tracking, a Nonlinear Model Predictive Control(NMPC) algorithm is proposed. Specifically, the Hammerstein-Wiener structure is employed to model the nonlinear hydraulic system, with parameters identified from experimental data. Based on this model, an NMPC trajectory tracking algorithm is developed, which accounts for control signal and actuator constraints. To mitigate the inherent 0.5-second response delay of the hydraulic system, a predictive delay compensation strategy is introduced, where the predicted joint states for the next 0.5 seconds serve as real-time control references. Simulation results indicate that, compared to the Proportional-Integral-Derivative(PID) and fuzzy PID algorithms, the NMPC significantly improves tracking accuracy, with the maximum error of the bucket end controlled within 20 cm. Field experiments were carried out using an autonomous excavator based on Robot Operating System (ROS), and the results confirm that the maximum error of trajectory tracking is within 50 cm. These results validate the feasibility and robustness of the proposed nonlinear model predictive controller under real-world operating conditions. Hydraulic system model Nonlinear Model Predictive Control Simulation Platform Trajectory Tracking Full Text Additional Declarations No competing interests reported. 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. 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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-7725337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530689450,"identity":"45323258-abf3-4c54-ba4d-1e0189932ed0","order_by":0,"name":"Jiwen Liu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Jiwen","middleName":"","lastName":"Liu","suffix":""},{"id":530689451,"identity":"f4debc72-dc61-4c5b-8e7f-dd155770896b","order_by":1,"name":"Yaxin Wu","email":"","orcid":"","institution":"Southeast 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