Learning Data-Driven PCHD Models for Control Engineering Applications*

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