高相对度安全约束下采样数据系统的对抗弹性
Adversarial Resilience for Sampled-Data Systems under High-Relative-Degree Safety Constraints
摘要：
控制障碍函数（CBF）最近已成为一个强大的方法，使所需的安全集前向不变的单和多智能体系统。在多代理的情况下，以前的文献已经考虑的情况下，所有代理合作，以确保相应的设置保持不变。然而，这些作品不考虑的情况下，一个子集的代理人的行为敌对的意图违反安全界限。此外，多智能体CBF的先前结果通常假设控制输入是连续的，不考虑采样数据动态。本文提出了一个框架，正常行为的代理在多智能体系统中的异构控制仿射，采样数据动态呈现一个安全的一套前向不变的对抗代理的存在。所提出的方法考虑了实际控制系统的几个方面，包括输入约束，时钟周期和干扰，以及控制输入的分布式计算。我们的方法还考虑功能描述安全集具有较高的相对程度，相对于系统动力学。这些结果的有效性，通过模拟证明。
英文摘要：
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.
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