Reduced-order modeling of LPV systems in the Loewner framework
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Abstract (arXiv)
We propose a model reduction method for LPV systems. We consider LPV state-space representations with an affine dependence on the scheduling variables. The main idea behind the proposed method is to compute the reduced order model in such a manner that its frequency domain transfer function coincides with that of the original model for some frequencies. The proposed method uses Loewner-like matrices, which can be calculated from the frequency domain representation of the system. The contribution of the paper represents an extension of the well-established Loewner framework to LPV models.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2021Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inferencecited by 16
- 2023PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEscited by 0
- 2024A finite-sample generalization bound for stable LPV systemscited by 0
- 2024A finite-sample generalization bound for stable LPV systems16 May 2024
- 2023PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs7 Jul 2023
- 2021Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference6 Jul 2021