🤖 AI Summary
Industrial process control demands interpretable and auditable strategies, a requirement that black-box neural controllers struggle to fulfill. This work proposes the first audit-driven controller synthesis framework tailored for industrial control: leveraging large language models to iteratively generate human-readable Python control programs under physics-informed simulation feedback. The approach integrates structured policy ideation, component-level multi-scenario feedback, and formal verification to automatically guarantee critical properties such as safety and monotonicity. A novel Luby-style universal restarting strategy is introduced to enable efficient, parameter-free search. Evaluated on a hot-rolling steel control task, the method achieves performance comparable to the best outcome from 730 manual tuning trials within only 160 iterations, while producing explicit, expert-auditable control logic.
📝 Abstract
Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, in which a language model iteratively proposes and refines human-readable Python controllers using rich behavioral feedback from a physics-based simulator. The framework combines structured strategic ideation, executable code generation, and per-component feedback across diverse operating conditions to search over control logic for height reduction, interpass time, and rolling velocity. Our first contribution is an auditable controller-synthesis pipeline for industrial process control. The generated controllers are explicit programs accessible to expert review, and we pair them with an automated audit pipeline that formally verifies key safety and monotonicity properties for the best synthesized heuristic. Our second contribution is a principled budget allocation strategy for LLM-driven heuristic search: we show that Luby-style universal restarts -- originally developed for randomized algorithms -- transfer directly to this setting, eliminating the need for problem-specific budget tuning. A single 160-iteration Luby campaign approaches the hindsight-optimal budget allocation derived from 52 ad-hoc runs totalling 730 iterations.