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.
@article{arxiv.2312.06217,
title = {Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems},
author = {Patrick J. W. Koelewijn and Rajiv Sing and Peter Seiler and Roland Tóth},
journal= {arXiv preprint arXiv:2312.06217},
year = {2024}
}
Comments
Accepted to the 20th IFAC Symposium on System Identification (SYSID 2024)