Learning-Enabled Robust Control with Noisy Measurements
Abstract
We present a constructive approach to bounded l2-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of H-infinity-observers paired with a recursively computed performance metric. We do not assume prior knowledge of a stabilizing controller.
Cite
Text
Kjellqvist and Rantzer. "Learning-Enabled Robust Control with Noisy Measurements." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[Kjellqvist and Rantzer. "Learning-Enabled Robust Control with Noisy Measurements." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/kjellqvist2022l4dc-learningenabled/)BibTeX
@inproceedings{kjellqvist2022l4dc-learningenabled,
title = {{Learning-Enabled Robust Control with Noisy Measurements}},
author = {Kjellqvist, Olle and Rantzer, Anders},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {86-96},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/kjellqvist2022l4dc-learningenabled/}
}