Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations

• URL: http://arxiv.org/abs/2311.07462v2
• Date: Mon, 25 Mar 2024 19:02:47 GMT
• Title: Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations
• Authors: Changjian Zhang, Parv Kapoor, Romulo Meira-Goes, David Garlan, Eunsuk Kang, Akila Ganlath, Shatadal Mishra, Nejib Ammar,
• Abstract summary: A critical attribute of cyber-physical systems (CPS) is robustness, denoting its capacity to operate safely. This paper proposes a novel specification-based robustness, which characterizes the effectiveness of a controller in meeting a specified system requirement. We present an innovative two-layer simulation-based analysis framework designed to identify subtle robustness violations.
• Score: 8.8690305802668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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