Learning Data-Driven PCHD Models for Control Engineering Applications
Abstract
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.
Cite
@article{arxiv.2204.09436,
title = {Learning Data-Driven PCHD Models for Control Engineering Applications},
author = {Annika Junker and Julia Timmermann and Ansgar Trächtler},
journal= {arXiv preprint arXiv:2204.09436},
year = {2023}
}
Comments
accepted for: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022) \c{opyright} 2022 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND