Adversarial Resilience for Sampled-Data Systems under High-Relative-Degree Safety Constraints

TL;DR

This paper develops a framework to ensure safety in multi-agent sampled-data systems with adversarial agents, considering high-relative-degree safety constraints, input limits, asynchrony, and disturbances, validated through simulations.

Contribution

It introduces a novel method for maintaining safety in multi-agent systems with adversarial agents under sampled-data and high-relative-degree constraints, addressing practical control challenges.

Findings

Framework effectively maintains safety despite adversarial agents.

Method handles high-relative-degree safety functions in sampled-data systems.

Simulations demonstrate robustness and practical applicability.

Abstract

Control barrier functions (CBFs) have recently become a powerful method for rendering desired safe sets forward invariant in single- and multi-agent systems. In the multi-agent 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 typically assume that control inputs are continuous and do not consider sampled-data dynamics. This paper presents a framework 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 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.