Nonlinear Data-Enabled Prediction and Control
Abstract
Behavioral theory, which characterizes linear dynamics with measured trajectories, has found successful applications in controller design and signal processing. However, the extension of behavioral theory to general nonlinear system remains an open question. In this work, we propose to apply behavioral theory to a reproducing kernel Hilbert space in order to extend its application to a class of nonlinear systems and we show its application in prediction and in predictive control.
Cite
Text
Lian and Jones. "Nonlinear Data-Enabled Prediction and Control." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Lian and Jones. "Nonlinear Data-Enabled Prediction and Control." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/lian2021l4dc-nonlinear/)BibTeX
@inproceedings{lian2021l4dc-nonlinear,
title = {{Nonlinear Data-Enabled Prediction and Control}},
author = {Lian, Yingzhao and Jones, Colin N.},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {523-534},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/lian2021l4dc-nonlinear/}
}