Predictive Control Barrier Functions for Online Safety Critical Control.

Abstract

This paper presents a methodology for constructing Control Barrier Functions (CBFs) that proactively consider the future safety of a system along a nominal trajectory, and effect corrective action before the trajectory leaves a designated safe set. Specifically, this paper presents a systematic approach for propagating a nominal trajectory on a receding horizon, and then encoding the future safety of this trajectory into a CBF. If the propagated trajectory is unsafe, then a controller satisfying the CBF condition will modify the nominal trajectory before the trajectory becomes unsafe. Compared to existing CBF techniques, this strategy is proactive rather than reactive and thus potentially results in smaller modifications to the nominal trajectory. The proposed strategy is shown to be provably safe, and then is demonstrated in simulated scenarios where it would otherwise be difficult to construct a traditional CBF. In simulation, the predictive CBF results in less modification to the nominal trajectory and smaller control inputs than a traditional CBF, and faster computations than a nonlinear model predictive control approach.

Bib

@inproceedings{DBLP:conf/cdc/BreedenP22,
  author       = {Joseph Breeden and
                  Dimitra Panagou},
  title        = {Predictive Control Barrier Functions for Online Safety Critical Control},
  booktitle    = {61st {IEEE} Conference on Decision and Control, {CDC} 2022, Cancun,
                  Mexico, December 6-9, 2022},
  pages        = {924--931},
  publisher    = {{IEEE}},
  year         = {2022},
  url          = {https://doi.org/10.1109/CDC51059.2022.9992926},
  doi          = {10.1109/CDC51059.2022.9992926},
  timestamp    = {Wed, 18 Jan 2023 15:37:50 +0100},
  biburl       = {https://dblp.org/rec/conf/cdc/BreedenP22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}