Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators
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.
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.Additional details
- Eprint ID
- 107467
- Resolver ID
- CaltechAUTHORS:20210113-163505361
- Raytheon Company
- Aker Scholarship Foundation
- Created
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2021-01-14Created from EPrint's datestamp field
- Updated
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2021-03-31Created from EPrint's last_modified field
- Caltech groups
- Center for Autonomous Systems and Technologies (CAST)