Adversarial resilience for sampled-data systems under high-relative-degree safety constraints

Abstract

Control barrier functions (CBFs) have recently become a powerful method for rendering desired safe sets forward invariant in single-agent and multiagent 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 multiagent CBFs typically assume that control inputs are continuous and do not consider sampled-data dynamics. This article presents a framework for normally behaving agents in a multiagent system with heterogeneous control-affine, sampled-data dynamics to render a safe set forward invariant in the presence of adversarial agents. The proposed approach considers several aspects of practical control systems including input constraints, clock asynchrony and disturbances, and distributed calculation of control inputs. Our approach also considers functions describing safe sets having high relative degree with respect to system dynamics. The efficacy of these results are demonstrated through simulations.

James Usevitch

Autonomous Systems Researcher

My research focuses on developing safe, intelligent, robust, and resilient multi-agent autonomous systems.