{"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/stable-gaussian-process-based-tracking","title":"Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems","arxiv_id":"1806.07190","date":"2018-06-19","proceeding":null,"authors":["Thomas Beckers","Dana Kulić","Sandra Hirche"],"abstract":"Perfect tracking control for real-world Euler-Lagrange systems is challenging\ndue to uncertainties in the system model and external disturbances. The\nmagnitude of the tracking error can be reduced either by increasing the\nfeedback gains or improving the model of the system. The latter is clearly\npreferable as it allows to maintain good tracking performance at low feedback\ngains. However, accurate models are often difficult to obtain. In this article,\nwe address the problem of stable high-performance tracking control for unknown\nEuler-Lagrange systems. In particular, we employ Gaussian Process regression to\nobtain a data-driven model that is used for the feed-forward compensation of\nunknown dynamics of the system. The model fidelity is used to adapt the\nfeedback gains allowing low feedback gains in state space regions of high model\nconfidence. The proposed control law guarantees a globally bounded tracking\nerror with a specific probability. Simulation studies demonstrate the\nsuperiority over state of the art tracking control approaches.","url_abs":"http://arxiv.org/abs/1806.07190v2","url_pdf":"http://arxiv.org/pdf/1806.07190v2.pdf","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":"stable-gaussian-process-based-tracking","repo_url":"https://github.com/TBeckers/CTC_GPR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}