Predictive safety filter using system level synthesis

Antoine Leeman, Johannes Köhler, Samir Bennani, Melanie Zeilinger
Proceedings of The 5th Annual Learning for Dynamics and Control Conference, PMLR 211:1180-1192, 2023.

Abstract

Safety filters provide modular techniques to augment {potentially} unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example.

Cite this Paper

BibTeX
@InProceedings{pmlr-v211-leeman23a, title = {Predictive safety filter using system level synthesis}, author = {Leeman, Antoine and K\"ohler, Johannes and Bennani, Samir and Zeilinger, Melanie}, booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference}, pages = {1180--1192}, year = {2023}, editor = {Matni, Nikolai and Morari, Manfred and Pappas, George J.}, volume = {211}, series = {Proceedings of Machine Learning Research}, month = {15--16 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v211/leeman23a/leeman23a.pdf}, url = {https://proceedings.mlr.press/v211/leeman23a.html}, abstract = {Safety filters provide modular techniques to augment {potentially} unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example. } }
Endnote
%0 Conference Paper %T Predictive safety filter using system level synthesis %A Antoine Leeman %A Johannes Köhler %A Samir Bennani %A Melanie Zeilinger %B Proceedings of The 5th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2023 %E Nikolai Matni %E Manfred Morari %E George J. Pappas %F pmlr-v211-leeman23a %I PMLR %P 1180--1192 %U https://proceedings.mlr.press/v211/leeman23a.html %V 211 %X Safety filters provide modular techniques to augment {potentially} unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example.
APA
Leeman, A., Köhler, J., Bennani, S. & Zeilinger, M.. (2023). Predictive safety filter using system level synthesis. Proceedings of The 5th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 211:1180-1192 Available from https://proceedings.mlr.press/v211/leeman23a.html.

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