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.

翻译：基于扩展动态模态分解的数据驱动动力学系统代理模型已在应用中得到广泛确立与普及。针对状态与控制量呈现乘性耦合的非完整系统，双线性代理模型已被证明具有显著优势。然而，目前仍缺乏基于仿真与实验数据对近似精度及其与不同超参数依赖关系的深入分析。本文通过研究差速驱动移动机器人来弥补这一空白，并首次提出数据高效代理模型系统性设计的指导准则。