Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators
| Autor: | Aaron D. Ames, Yuxiao Chen, Carl Folkestad, Joel W. Burdick |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Mobile robot 02 engineering and technology 01 natural sciences Matrix multiplication 010305 fluids & plasmas Data modeling System dynamics Data-driven Computer Science::Robotics 020901 industrial engineering & automation Control and Systems Engineering Control theory Backup 0103 physical sciences Robot Sensitivity (control systems) Invariant (mathematics) |
| Zdroj: | ACC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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