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arXiv · 2312.06217

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

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

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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Patrick J. W. Koelewijn, Rajiv Sing, Peter Seiler, Roland Tóth. 2023-12-11. Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems. https://arxiv.org/abs/2312.06217

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