On Koopman-based surrogate models for non-holonomic robots Bold, Lea Rosenfelder, Mario Eschmann, Hannes Ebel, Henrik Worthmann, Karl 4. IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MICNON) (Lyon, 04.-06.09.2024) article article article article article Text https://doi.org/10.1016/j.ifacol.2024.10.142 https://www.db-thueringen.de/receive/dbt_mods_00069126 https://www.db-thueringen.de/receive/dbt_mods_00069126 doc-type:Article ScholarlyArticle ddc:600 control-affine -- DMD -- hardware -- Koopman -- learning -- Mobile robots -- non-holonomic Data-driven surrogate models of dynamical systems based on the extended dynamic mode decomposition are nowadays well-established and widely applied. Further, for non-holonomic systems exhibiting a multiplicative coupling between states and controls, the usage of bilinear surrogate models has proven beneficial. However, an analysis of the approximation quality and its dependence on different hyperparameters, including physics-motivated dictionary choices, with real-world experimental data is still missing. To close this gap and due to its high practical relevance and widespread usage in applications such as service robotics, we investigate Koopman-based surrogate modeling for a differential-drive mobile robot, also in hardware. 2024-10-30 Elsevier 6 Seiten eng IFAC-PapersOnLine -- IFAC papers online -- 10.1016/j.ifacol -- http://uri.gbv.de/document/gvk:ppn:839396090 -- 2405-8963 -- 2839185-8 public https://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess