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：随着网络-物理系统（CPS）在复杂物理环境中的广泛应用，其已涵盖自动驾驶、物联网（IoT）及智慧城市等领域。鲁棒性作为CPS的关键属性，表征系统在运行环境存在潜在干扰与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，通过信号时态逻辑（STL）形式化描述控制器满足特定系统需求的有效性，同时兼顾系统中可能存在的偏差。基于该定义，本文进一步提出鲁棒性反证问题，旨在识别可能违反规约需求的最小偏差。我们构建了一种创新的双层仿真分析框架，用于检测微妙的鲁棒性违反行为。为评估所提方法，我们设计了一系列基准测试问题，其中系统参数可被调节以模拟各类不确定性与干扰。初步评估表明，该反证方法能够有效识别鲁棒性违反情况，为对比传统控制器与基于强化学习（RL）控制器之间的鲁棒性差异提供了重要洞见。