Structure-preserving discretization for port-Hamiltonian descriptor systems
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Abstract (arXiv)
We extend the modeling framework of port-Hamiltonian descriptor systems to include under- and over-determined systems and arbitrary differentiable Hamiltonian functions. This structure is associated with a Dirac structure that encloses its energy balance properties. In particular, port-Hamiltonian systems are naturally passive and Lyapunov stable, because the Hamiltonian defines a Lyapunov function. The explicit representation of input and dissipation in the structure make these systems particularly suitable for output feedback control. It is shown that this structure is invariant under a wide class of nonlinear transformations, and that it can be naturally modularized, making it adequate for automated modeling. We investigate then the application of time-discretization schemes to these systems and we show that, under certain assumptions on the Hamiltonian, structure preservation is achieved for some methods. Numerical examples are provided.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Mar 2019.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemescited by 3
- 2024Data-driven identification of port-Hamiltonian DAE systems by Gaussian processescited by 2
- 2024Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networkscited by 1
- 2024Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks15 Dec 2024
- 2024Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes26 Jun 2024
- 2023Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes11 Oct 2023