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Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators

Folkestad, Carl and Chen, Yuxiao and Ames, Aaron D. and Burdick, Joel W. (2021) Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators. IEEE Control Systems Letters, 5 (6). pp. 2012-2017. ISSN 2475-1456. doi:10.1109/lcsys.2020.3046159. https://resolver.caltech.edu/CaltechAUTHORS:20210113-163505361

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Abstract

Control barrier functions (CBFs) are a powerful tool to guarantee safety of autonomous systems, yet they rely on the computation of control invariant sets, which is notoriously difficult. A backup strategy employs an implicit control invariant set computed by forward integrating the system dynamics. However, this integration is prohibitively expensive for high dimensional systems, and inaccurate in the presence of unmodelled dynamics. We propose to learn discrete-time Koopman operators of the closed-loop dynamics under a backup strategy. This approach replaces forward integration by a simple matrix multiplication, which can mostly be computed offline. We also derive an error bound on the unmodeled dynamics in order to robustify the CBF controller. Our approach extends to multi-agent systems, and we demonstrate the method on collision avoidance for wheeled robots and quadrotors.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/lcsys.2020.3046159DOIArticle
ORCID:
AuthorORCID
Folkestad, Carl0000-0002-3436-8247
Chen, Yuxiao0000-0001-5276-7156
Ames, Aaron D.0000-0003-0848-3177
Burdick, Joel W.0000-0002-3091-540X
Additional Information:© 2020 IEEE. Manuscript received September 14, 2020; revised November 21, 2020; accepted December 11, 2020. Date of publication December 21, 2020; date of current version March 22, 2021. This work was supported in part by Raytheon Technologies. The work of Carl Folkestad was supported by the Aker Scholarship Foundation. Recommended by Senior Editor F. Dabben.
Group:Center for Autonomous Systems and Technologies (CAST)
Funders:
Funding AgencyGrant Number
Raytheon CompanyUNSPECIFIED
Aker Scholarship FoundationUNSPECIFIED
Subject Keywords:Robotics, computational methods, supervisory control
Issue or Number:6
DOI:10.1109/lcsys.2020.3046159
Record Number:CaltechAUTHORS:20210113-163505361
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210113-163505361
Official Citation:C. Folkestad, Y. Chen, A. D. Ames and J. W. Burdick, "Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators," in IEEE Control Systems Letters, vol. 5, no. 6, pp. 2012-2017, Dec. 2021, doi: 10.1109/LCSYS.2020.3046159
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107467
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Jan 2021 17:51
Last Modified:31 Mar 2021 22:14

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