Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

M. Scandella, M. Mazzoleni, S. Formentin, F. Previdi

Abstract

In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. However, for such models, model selection is a hard task if no prior physical knowledge is given. In this paper, we propose a non-parametric approach to infer a continuous-time linear model from data, by automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. By means of benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, also in the critical case when short sequences of canonical input signals, like impulses or steps, are used for model learning. [Paper, Code]

Reference

M. Scandella, M. Mazzoleni, S. Formentin and F. Previdi, "Kernel-based identification of asymptotically stable continuous-time linear dynamical systems," in International Journal of Control, vol. 0, no. 0, pp. 1-14, Feb. 2021, doi: 10.1080/00207179.2020.1868580.

Bibtex

@article{doi:10.1080/00207179.2020.1868580,
author = { Matteo   Scandella  and  Mirko   Mazzoleni  and  Simone   Formentin  and  Fabio   Previdi },
title = {Kernel-based identification of asymptotically stable continuous-time linear dynamical systems},
journal = {International Journal of Control},
volume = {0},
number = {0},
pages = {1-14},
year  = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/00207179.2020.1868580},
}