LLM-Enhanced Symbolic Control for Safety-Critical Applications
(2025) IFAC-ICONS — Location: Padova, Italy (15.September.2025)
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- Abstract
- Motivated by Smart Manufacturing and Industry 4.0, we introduce a framework for synthesizing Abstraction-Based Controller Design (ABCD) for reach-avoid problems from Natural Language (NL) specifications using Large Language Models (LLMs). A Code Agent interprets an NL description of the control problem and translates it into a formal language interpretable by state-of-the-art symbolic control software, while a Checker Agent verifies the correctness of the generated code and enhances safety by identifying specification mismatches. Evaluations show that the proposed approach increases the success rate of solving reach-avoid problems from 20%, when using LLMs directly, to 80%, while also enhancing robustness to linguistic variability across test cases. The proposed approach lowers the barrier to formal control synthesis by enabling intuitive, NL-based task definition while maintaining safety guarantees through automated validation.
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Citations
Bayat, A., Abate, A., Ozay, N., & Jungers, R. (2025). LLM-Enhanced Symbolic Control for Safety-Critical Applications. Published. IFAC-ICONS, Padova, Italy. https://doi.org/10.1016/j.ifacol.2025.12.008