Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems

Authors

Abstract

Given a sufficiently numerous set of vector-exponential trajectories of a conservative port-Hamiltonian system and the supply rate, we compute a corresponding set of state trajectories by factorizing a constant Pick-like matrix. State equations are then obtained by solving a system of linear equations involving the system trajectories and the computed state ones. If a factorization of only a principal submatrix of the Pick matrix is performed, our procedure yields a lower-order conservative port-Hamiltonian model obtained by projection of the full-order one. We also describe a similar approach to identification and model-order reduction for self-adjoint Hamiltonian systems.

Citation

Journal: 52nd IEEE Conference on Decision and Control
Year: 2013
Volume:
Issue:
Pages: 145–150
Publisher: IEEE
DOI: 10.1109/cdc.2013.6759873

BibTeX

@inproceedings{Rapisarda_2013,
  title={{Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems}},
  DOI={10.1109/cdc.2013.6759873},
  booktitle={{52nd IEEE Conference on Decision and Control}},
  publisher={IEEE},
  author={Rapisarda, Paolo and van der Schaft, Arjan},
  year={2013},
  pages={145--150}
}

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References

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