Integrated RBF-EKF Observer and MPC for Simultaneous Trajectory Tracking and Stability Control of 4WID-EVs

Integrated RBF-EKF Observer and MPC for Simultaneous Trajectory Tracking and Stability Control of 4WID-EVs | 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 Integrated RBF-EKF Observer and MPC for Simultaneous Trajectory Tracking and Stability Control of 4WID-EVs Meng Dang, Chuanwei Zhang, Jianlong Wang, Yansong Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8075646/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Accurate trajectory tracking and stable motion control under extreme operating conditions remain significant challenges for Four-Wheel-Independent-Drive Electric Vehicles (4WID-EVs). To address these issues, this paper focuses on state estimation and trajectory tracking control, proposing novel algorithms and conducting comprehensive simulations and experiments. Firstly, a high-fidelity 7-Degree-of-Freedom (7-DOF) vehicle dynamics model is established and validated against a commercial CarSim model, providing a reliable platform for controller design. Secondly, to tackle the difficulty in directly measuring key states like the sideslip angle, a hybrid observer that fuses a Radial Basis Function Neural Network (RBFNN) with an Extended Kalman Filter (EKF) is proposed. The RBFNN, optimized via the control variates method, generates a "pseudo-sideslip angle," which is then fed as an observation into the EKF, significantly enhancing estimation accuracy. Thirdly, a Linear Time-Varying Model Predictive Control (LTV-MPC) based trajectory tracking controller is designed. The nonlinear vehicle model is linearized at each sampling point, transforming the optimal control problem into a Quadratic Programming (QP) problem. Crucially, explicit mathematical relationships between control variables and stability indices (sideslip angle, tire slip angle) are derived and embedded as stability constraints within the optimization framework, effectively coordinating tracking accuracy with vehicle stability. Finally, co-simulations (CarSim/Simulink) and Hardware-in-the-Loop (HIL) tests on a dSPACE platform are performed. Results demonstrate that the proposed RBF-EKF observer reduces the Root-Mean-Square Error (RMSE) of sideslip angle estimation by up to 30.43% compared to the standard EKF. Furthermore, the proposed Model Predictive Control (MPC) controller outperforms the traditional Preview Driver Model (PDM), reducing the maximum lateral tracking error and yaw angle error by 0.30 m and 3.11°, respectively, in high-speed double-lane-change scenarios, while ensuring all states remain within stability boundaries. Physical sciences/Engineering Physical sciences/Mathematics and computing Four-Wheel-Independent-Drive Electric Vehicle State Estimation Trajectory Tracking Model Predictive Control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 17 Nov, 2025 Editor invited by journal 12 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 10 Nov, 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-8075646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550611558,"identity":"0469cd9b-2582-453c-b42c-b77ca0d68fc5","order_by":0,"name":"Meng 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To address these issues, this paper focuses on state estimation and trajectory tracking control, proposing novel algorithms and conducting comprehensive simulations and experiments. Firstly, a high-fidelity 7-Degree-of-Freedom (7-DOF) vehicle dynamics model is established and validated against a commercial CarSim model, providing a reliable platform for controller design. Secondly, to tackle the difficulty in directly measuring key states like the sideslip angle, a hybrid observer that fuses a Radial Basis Function Neural Network (RBFNN) with an Extended Kalman Filter (EKF) is proposed. The RBFNN, optimized via the control variates method, generates a \"pseudo-sideslip angle,\" which is then fed as an observation into the EKF, significantly enhancing estimation accuracy. Thirdly, a Linear Time-Varying Model Predictive Control (LTV-MPC) based trajectory tracking controller is designed. 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