  Within this work, we investigate how data-driven numerical approximation
methods of the Koopman operator can be used in practical control engineering
applications. We refer to the method Extended Dynamic Mode Decomposition
(EDMD), which approximates a nonlinear dynamical system as a linear model. This
makes the method ideal for control engineering applications, because a linear
system description is often assumed for this purpose. Using academic examples,
we simulatively analyze the prediction performance of the learned EDMD models
and show how relevant system properties like stability, controllability, and
observability are reflected by the EDMD model, which is a critical requirement
for a successful control design process. Subsequently, we present our
experimental results on a mechatronic test bench and evaluate the applicability
to the control engineering design process. As a result, the investigated
methods are suitable as a low-effort alternative for the design steps of model
building and adaptation in the classical model-based controller design method.

Systems and Control;Systems and Control;Optimization and Control