Adversarial Resilience for Sampled-Data Systems using Control Barrier Function Methods.

Abstract

Control barrier functions (CBFs) have recently become a powerful method for rendering a desired safe set forward invariant in single- and multi-agent systems. In the multiagent case, prior literature has considered scenarios where all agents cooperate to ensure that the corresponding set remains invariant. However, these works do not consider scenarios where a subset of the agents are behaving adversarially with the intent to violate safety bounds. In addition, prior results on multi-agent CBFs assume that control inputs are continuous and do not explicitly consider sampled-data dynamics. This paper presents a method for normally behaving agents in a multi-agent system with heterogeneous control-affine sampled-data dynamics to render a safe set forward invariant in the presence of adversarial agents. The efficacy of these results are demonstrated through simulations.

Bib

@inproceedings{DBLP:conf/amcc/UsevitchP21,
  author       = {James Usevitch and
                  Dimitra Panagou},
  title        = {Adversarial Resilience for Sampled-Data Systems using Control Barrier
                  Function Methods},
  booktitle    = {2021 American Control Conference, {ACC} 2021, New Orleans, LA, USA,
                  May 25-28, 2021},
  pages        = {758--763},
  publisher    = {{IEEE}},
  year         = {2021},
  url          = {https://doi.org/10.23919/ACC50511.2021.9482659},
  doi          = {10.23919/ACC50511.2021.9482659},
  timestamp    = {Fri, 30 Jul 2021 11:11:53 +0200},
  biburl       = {https://dblp.org/rec/conf/amcc/UsevitchP21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}