# Reduced-order modeling of LPV systems in the Loewner framework

arXiv: https://arxiv.org/abs/2104.10767 · HTML: https://mlsanity.com/paper/2104.10767/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference](https://mlsanity.com/paper/2107.02597/) (2021)
- [PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs](https://mlsanity.com/paper/2307.03630/) (2023)
- [A finite-sample generalization bound for stable LPV systems](https://mlsanity.com/paper/2405.10054/) (2024)

## 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.