arXiv Open Access 2023

Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems

Patrick J. W. Koelewijn Rajiv Sing Peter Seiler Roland Tóth

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Abstrak

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples.

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Penulis (4)

Patrick J. W. Koelewijn

Rajiv Sing

Peter Seiler

Roland Tóth

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APA MLA BibTeX

Koelewijn, P.J.W., Sing, R., Seiler, P., Tóth, R. (2023). Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems. https://arxiv.org/abs/2312.06217

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓