Physics-informed Learning for Passivity-based Tracking Control

Authors

Thomas Beckers, Leonardo Colombo

Abstract

Passivity-based control ensures system stability by leveraging dissipative properties and is widely applied in electrical and mechanical systems. Port-Hamiltonian systems (PHS), in particular, are well-suited for interconnection and damping assignment passivity-based control (IDA-PBC) due to their structured, energy-centric modeling approach. However, current IDA-PBC faces two key challenges: (i) it requires precise system knowledge, which is often unavailable due to model uncertainties, and (ii) it is typically limited to set-point control. To address these limitations, we propose a data-driven tracking control approach based on a physics-informed model, namely Gaussian process port-Hamiltonian systems, along with the modified matching equation. By leveraging the Bayesian nature of the model, we establish probabilistic stability and passivity guarantees. A simulation demonstrates the effectiveness of our approach.

Citation

• Journal: 2025 IEEE 64th Conference on Decision and Control (CDC)
• Year: 2025
• Volume:
• Issue:
• Pages: 2091–2096
• Publisher: IEEE
• DOI: 10.1109/cdc57313.2025.11312152

BibTeX

@inproceedings{Beckers_2025,
  title={{Physics-informed Learning for Passivity-based Tracking Control}},
  DOI={10.1109/cdc57313.2025.11312152},
  booktitle={{2025 IEEE 64th Conference on Decision and Control (CDC)}},
  publisher={IEEE},
  author={Beckers, Thomas and Colombo, Leonardo},
  year={2025},
  pages={2091--2096}
}

Download the bib file

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