The adoption of cyber-physical systems (CPS) is on the rise in complex physical environments, encompassing domains such as autonomous vehicles, the Internet of Things (IoT), and smart cities. A critical attribute of CPS is robustness, denoting its capacity to operate safely despite potential disruptions and uncertainties in the operating environment. This paper proposes a novel specification-based robustness, which characterizes the effectiveness of a controller in meeting a specified system requirement, articulated through Signal Temporal Logic (STL) while accounting for possible deviations in the system. This paper also proposes the robustness falsification problem based on the definition, which involves identifying minor deviations capable of violating the specified requirement. We present an innovative two-layer simulation-based analysis framework designed to identify subtle robustness violations. To assess our methodology, we devise a series of benchmark problems wherein system parameters can be adjusted to emulate various forms of uncertainties and disturbances. Initial evaluations indicate that our falsification approach proficiently identifies robustness violations, providing valuable insights for comparing robustness between conventional and reinforcement learning (RL)-based controllers

翻译：网络物理系统在复杂物理环境中的应用日益增长，涵盖自主车辆、物联网和智慧城市等领域。其关键属性鲁棒性是指系统在面临潜在干扰和环境不确定性时仍能安全运行的能力。本文提出一种新颖的基于规约的鲁棒性概念，通过信号时序逻辑（STL）刻画控制器在满足给定系统规约时的有效性，同时考虑系统可能存在的偏差。基于该定义，本文进一步提出鲁棒性反证问题，旨在识别能够违反指定规约的最小偏差。我们提出一种创新的双层仿真分析框架，用于检测细微的鲁棒性违反。为评估该方法，我们设计了一系列基准测试问题，通过调整系统参数模拟多种形式的不确定性与扰动。初步评估表明，我们的反证方法能有效识别鲁棒性违反，为比较传统控制器与基于强化学习的控制器的鲁棒性提供重要见解。