Compositional Analysis of Parametric Cooperative Cyber-Physical Systems
This paper studies the parameterized compositional model checking problem (PCMCP) for analyzing global safety properties of cooperative Cyber-Physical Systems modeled as networks of hybrid automata. We develop the modular verification technique of PCMCP to analyze global safety properties of paramet...
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Published in | 2024 IEEE/ACM 12th International Conference on Formal Methods in Software Engineering (FormaliSE) pp. 77 - 87 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
ACM
14.04.2024
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Subjects | |
Online Access | Get full text |
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Summary: | This paper studies the parameterized compositional model checking problem (PCMCP) for analyzing global safety properties of cooperative Cyber-Physical Systems modeled as networks of hybrid automata. We develop the modular verification technique of PCMCP to analyze global safety properties of parametric models of distributed cooperative agents, where each agent is modeled as a hybrid I/O automaton (HIOA). Combining local symmetry amongst the agents, compositionality, and parametric analysis can avoid the computational cost associated with global analysis of the hybrid system's state space. PCMCP involves analyzing the local symmetry of the agent networks and the agents' continuous interfering interactions with their neighboring agents. This analysis identifies a representative agent (HIOA) from each locally symmetric equivalent class of the system. A key step in this analysis is the calculation of overapproximations of the reachable states set for representative agents in a small cutoff instance of the system that then generalizes to arbitrarily sized HIOA networks. We illustrate this technique by outlining its application to compute a compositional local invariant for a platoon of N cooperative adaptive cruise control (CACC) vehicles. We establish a platoon cutoff and show that our simplified CACC model is collision-free, which then generalizes to the entire platoon family. |
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ISSN: | 2575-5099 |