Abstraction of Elementary Hybrid Systems by Variable Transformation
Not on the map. Why?
Abstract (arXiv)
Elementary hybrid systems (EHSs) are those hybrid systems (HSs) containing elementary functions such as exp, ln, sin, cos, etc. EHSs are very common in practice, especially in safety-critical domains. Due to the non-polynomial expressions which lead to undecidable arithmetic, verification of EHSs is very hard. Existing approaches based on partition of state space or over-approximation of reachable sets suffer from state explosion or inflation of numerical errors. In this paper, we propose a symbolic abstraction approach that reduces EHSs to polynomial hybrid systems (PHSs), by replacing all non-polynomial terms with newly introduced variables. Thus the verification of EHSs is reduced to the one of PHSs, enabling us to apply all the well-established verification techniques and tools for PHSs to EHSs. In this way, it is possible to avoid the limitations of many existing methods. We illustrate the abstraction approach and its application in safety verification of EHSs by several real world examples.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learningcited by 11
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagationcited by 2
- 2025Data-driven system identification using quadratic embeddings of nonlinear dynamicscited by 1
- 2026Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systemscited by 0
- 2026Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems4 May 2026
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation9 Nov 2025
- 2025Data-driven system identification using quadratic embeddings of nonlinear dynamics14 Jan 2025
- 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learning28 Jan 2022