  The design of control engineering applications usually requires a model that
accurately represents the dynamics of the real system. In addition to classical
physical modeling, powerful data-driven approaches are increasingly used.
However, the resulting models are not necessarily in a form that is
advantageous for controller design. In the control engineering domain, it is
highly beneficial if the system dynamics is given in PCHD form (Port-Controlled
Hamiltonian Systems with Dissipation) because globally stable control laws can
be easily realized while physical interpretability is guaranteed. In this work,
we exploit the advantages of both strategies and present a new framework to
obtain nonlinear high accurate system models in a data-driven way that are
directly in PCHD form. We demonstrate the success of our method by model-based
application on an academic example, as well as experimentally on a test bed.
