Robust port-Hamiltonian representations of passive systems
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Abstract (arXiv)
We discuss the problem of robust representations of stable and passive transfer functions in particular coordinate systems, and focus in particular on the so-called port-Hamiltonian representations. Such representations are typically far from unique and the degrees of freedom are related to the solution set of the so-called Kalman-Yakubovich-Popov linear matrix inequality (LMI). In this paper we analyze robustness measures for the different possible representations and relate it to quality functions defined in terms of the eigenvalues of the matrix associated with the LMI. In particular, we look at the analytic center of this LMI. From this, we then derive inequalities for the passivity radius of the given model representation.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2018.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2022Pseudo-Hamiltonian Neural Networks with State-Dependent External Forcescited by 17
- 2022Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systemscited by 11
- 2022Thermodynamics of learning physical phenomenacited by 10
- 2024Data-driven identification of latent port-Hamiltonian systemscited by 3
- 2024Data-driven identification of latent port-Hamiltonian systems15 Aug 2024
- 2022Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems3 Nov 2022
- 2022Thermodynamics of learning physical phenomena26 Jul 2022
- 2022Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces6 Jun 2022