Data-driven surrogate models of dynamical systems based on the extended dynamic mode decomposition are nowadays well-established and widespread in applications. Further, for non-holonomic systems exhibiting a multiplicative coupling between states and controls, the usage of bi-linear surrogate models has proven beneficial. However, an in-depth analysis of the approximation quality and its dependence on different hyperparameters based on both simulation and experimental data is still missing. We investigate a differential-drive mobile robot to close this gap and provide first guidelines on the systematic design of data-efficient surrogate models.
@article{arxiv.2303.09144,
title = {On Koopman-based surrogate models for non-holonomic robots},
author = {Lea Bold and Hannes Eschmann and Mario Rosenfelder and Henrik Ebel and Karl Worthmann},
journal= {arXiv preprint arXiv:2303.09144},
year = {2023}
}