Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach

Mirko Mazzoleni, Matteo Scandella, Simone Formentin, Fabio Previdi
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:267-276, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v120-mazzoleni20a, 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}, pages = {267--276}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/mazzoleni20a/mazzoleni20a.pdf}, url = {https://proceedings.mlr.press/v120/mazzoleni20a.html}, 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.} }
Endnote
%0 Conference Paper %T Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach %A Mirko Mazzoleni %A Matteo Scandella %A Simone Formentin %A Fabio Previdi %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-mazzoleni20a %I PMLR %P 267--276 %U https://proceedings.mlr.press/v120/mazzoleni20a.html %V 120 %X 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.
APA
Mazzoleni, M., Scandella, M., Formentin, S. & Previdi, F.. (2020). 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, in Proceedings of Machine Learning Research 120:267-276 Available from https://proceedings.mlr.press/v120/mazzoleni20a.html.

Related Material