@inproceedings{4a48a20dcdac48a0a55b42d949f626b3,
title = "An Online Adaptation Strategy for Direct Data-driven Control",
abstract = "The fundamental lemma from behavioral systems theory yields a data-driven nonparametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear systems by updating the system representation during control. Unlike existing approaches, our method does not impose suitable excitation to the control inputs, but runs as an observer parallel to the controller. Whenever a rank condition is deemed to be fulfilled, the system representation is updated using newly available datapoints. In a reference tracking simulation of a two-link robotic arm, we showcase the performance of the proposed strategy in a predictive control framework.",
keywords = "Data-based control, Data-driven optimal control, Identification for control, Online adaptation, Predictive control, Uncertain systems",
author = "Johannes Teutsch and Sebastian Ellmaier and Sebastian Kerz and Dirk Wollherr and Marion Leibold",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.1640",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "644--649",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
edition = "2",
}