Learning Data-Driven PCHD Models for Control Engineering Applications

TL;DR

This paper introduces a novel data-driven framework to derive nonlinear system models directly in PCHD form, facilitating control design with stability and interpretability, demonstrated through academic and experimental tests.

Contribution

It presents a new method to obtain high-accuracy nonlinear models in PCHD form directly from data, combining data-driven modeling with control-friendly structure.

Findings

Successful modeling of systems in PCHD form from data

Application demonstrated on academic and test bed systems

Models enable stable control law design

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.