Black-Box Continuous-Time Transfer Function Estimation with Stability Guarantees: A Kernel-Based Approach
Abstract
Continuous-time parametric models of dynamical systems are usually preferred given their physical interpretation. When there is a lack of prior physical knowledge, the user is faced with the model selection issue. In this paper, we propose a non-parametric approach to estimate a continuous-time stable linear model from data, while automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. Results show how the proposed approach outperforms the state of the art.
Cite
Text
Mazzoleni et al. "Black-Box Continuous-Time Transfer Function Estimation with Stability Guarantees: A Kernel-Based Approach." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Mazzoleni et al. "Black-Box Continuous-Time Transfer Function Estimation with Stability Guarantees: A Kernel-Based Approach." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/mazzoleni2020l4dc-blackbox/)BibTeX
@inproceedings{mazzoleni2020l4dc-blackbox,
title = {{Black-Box Continuous-Time Transfer Function Estimation with Stability Guarantees: A Kernel-Based Approach}},
author = {Mazzoleni, Mirko and Scandella, Matteo and Formentin, Simone and Previdi, Fabio},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {267-276},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/mazzoleni2020l4dc-blackbox/}
}