{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-based-model-predictive-control-with","title":"Learning-based model predictive control with moving horizon state estimation for autonomous racing","arxiv_id":null,"date":"2024-09-28","proceeding":"International Journal of Control 2024 9","authors":["Yassine Kebbati","Andreas Rauh","Naima Ait-Oufroukh","Dalil Ichalal","Vincent Vigneron"],"abstract":"This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed controller is further enhanced with a learning extension based on Gaussian process regression that improves model predictions. The proposed control, estimation, and planning schemes are evaluated on two different race tracks.","url_abs":"https://doi.org/10.1080/00207179.2024.2409305","url_pdf":"https://univ-evry.hal.science/hal-04745064","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-based-model-predictive-control-with","repo_url":"https://github.com/yassinekebbati/GP_Learning-based_MPC_with_MHE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-racing","task_name":"Autonomous Racing"},{"task_slug":"model-predictive-control","task_name":"Model Predictive Control"},{"task_slug":"state-estimation","task_name":"State Estimation"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}