Learning-Enabled Robust Control with Noisy Measurements

Olle Kjellqvist, Anders Rantzer
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:86-96, 2022.

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 this Paper

BibTeX
@InProceedings{pmlr-v168-kjellqvist22a, 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}, pages = {86--96}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/kjellqvist22a/kjellqvist22a.pdf}, url = {https://proceedings.mlr.press/v168/kjellqvist22a.html}, 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.} }
Endnote
%0 Conference Paper %T Learning-Enabled Robust Control with Noisy Measurements %A Olle Kjellqvist %A Anders Rantzer %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-kjellqvist22a %I PMLR %P 86--96 %U https://proceedings.mlr.press/v168/kjellqvist22a.html %V 168 %X 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.
APA
Kjellqvist, O. & Rantzer, A.. (2022). Learning-Enabled Robust Control with Noisy Measurements. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:86-96 Available from https://proceedings.mlr.press/v168/kjellqvist22a.html.

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