On Koopman-based surrogate models for non-holonomic robots

Abstract

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.