Data-Driven Abstraction of Monotone Systems
Abstract
In this paper, we introduce an approach for data-driven abstraction of monotone dynamical systems. First, we present an approach to find the optimal approximation of the dynamics of an unknown system by a set-valued map based on a set of transitions generated by the system. Then we show that the dynamical system induced by the introduced map is equivalent (in the sense of alternating bisimulation) to a finite state transition system which can be used to synthesize controllers using the well-established symbolic control techniques. We show the effectiveness of the approach on a safety controller synthesis problem.
Cite
Text
Makdesi et al. "Data-Driven Abstraction of Monotone Systems." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Makdesi et al. "Data-Driven Abstraction of Monotone Systems." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/makdesi2021l4dc-datadriven/)BibTeX
@inproceedings{makdesi2021l4dc-datadriven,
title = {{Data-Driven Abstraction of Monotone Systems}},
author = {Makdesi, Anas and Girard, Antoine and Fribourg, Laurent},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {803-814},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/makdesi2021l4dc-datadriven/}
}