Modeling and Verification of Uncertain Cyber-Physical System Based on Decision Processes

Na Chen, Shengling Geng () and Yongming Li
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Na Chen: The College of Computer, Qinghai Normal University, Xining 810008, China
Shengling Geng: The College of Computer, Qinghai Normal University, Xining 810008, China
Yongming Li: The College of Computer, Qinghai Normal University, Xining 810008, China

Mathematics, 2023, vol. 11, issue 19, 1-20

Abstract: Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty.

Keywords: cyber-physical system; uncertain hybrid timed automaton; generalized possibilistic decision processes; scheduler; activity; safety; Ptolemy II; modeling; simulation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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