A Pragmatic Guide to Building Conservative Discrete Abstractions of Cyber-Physical Systems

2026-08-10

AI Summary

This work addresses the challenges of transferability and computational feasibility in discrete abstraction for symbolic model checking of cyber-physical systems by proposing a conservatism-first, four-step modular workflow to construct finite-state abstractions of closed-loop dynamical systems. The approach integrates state partitioning, conservative transition construction, spurious behavior elimination, and specification semantics lifting, enabling composable and replaceable subroutine design. Transition relations are built using axis-aligned bounding boxes, polyhedra, and sampling with PAC coverage certificates, combined with certified erasure and counterexample-guided refinement. Reliable lifting of LTL specifications is achieved through may–must semantics. Evaluation across three case studies demonstrates that the workflow effectively balances abstraction accuracy and verification efficiency while clearly revealing the impact of different design choices on the outcomes.

Abstract

Symbolic model checking is an effective approach for verifying semantically rich temporal-logic properties of cyber-physical systems, but it hinges on discretizing continuous-state dynamics into a finite-state abstraction. To transfer verification guarantees from the abstract model to the concrete CPS, the abstraction must conservatively approximate the concrete state space and behaviors. Hence, model-builders must maintain this soundness while balancing pessimism with tractability. However, they face several common pitfalls such as under-approximating the state space, under-approximating transitions, unsound pruning of "degenerate" behaviors, and improper specification lifting. This tutorial presents a pragmatic, conservative-by-construction workflow for building discrete abstractions of closed-loop dynamical systems. The workflow consists of four modular steps with interchangeable subroutines: (i) state-space partition and abstraction-function design, (ii) conservative transition construction via axis-aligned bounding boxes, polytopes, or sampling with PAC coverage certificates, (iii) mitigation of spurious transitions and self-loops using certified erasure and counterexample-guided abstraction refinement, and (iv) sound lifting of LTL specifications using may-must semantics. We demonstrate the end-to-end pipeline on three case studies and report how these design choices affect abstraction structure, runtime, and verification outcomes.

Problem

• cyber-physical systems
• discrete abstraction
• symbolic model checking
• conservative approximation
• formal verification

Innovation

• conservative abstraction
• symbolic model checking
• cyber-physical systems
• counterexample-guided refinement
• PAC coverage