Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

Researchers demonstrate that on-premise open-source large language models can serve as structural priors for tuning complex industrial control systems, particularly excelling on strongly coupled MIMO systems where traditional methods fail. The approach achieves superior sample efficiency and interpretability compared to classical optimization, reaching near-optimal controller tuning in 18 evaluations versus hundreds needed by global optimizers.

This research addresses a longstanding challenge in industrial control engineering: tuning controllers for tightly coupled multi-input multi-output systems where decentralized classical methods struggle with loop interactions and non-convex optimization landscapes. The study reveals a nuanced role for large language models—not as direct optimizers, but as intelligent structural reasoners that propose effective controller architectures.

The experimental design brilliantly isolates when LLMs add value. On simple single-loop systems, classical relay-feedback tuning outperforms LLM suggestions, establishing that the approach isn't universally superior. However, on complex coupled systems like the quadruple-tank problem with conflicting objectives, the results invert dramatically. Where naive optimization and global methods fail entirely, a scaffolded LLM produces interpretable, counter-intuitive controller structures that achieve superior performance when refined with classical optimization.

The practical implications for industrial automation are substantial. Sample efficiency matters enormously in real plants where each evaluation risks operational disruption or cost. The LLM's ability to reach usable controllers in 18 evaluations—versus worse-than-open-loop performance from differential evolution at that stage—demonstrates genuine engineering value. The approach scales favorably with system dimension, showing ~6x efficiency gains on 3x3 plants, suggesting increasing utility for complex industrial processes.

The reproducible benchmark methodology strengthens the contribution significantly. By clearly delimiting when open LLMs help (coupled systems with structural complexity) versus when they don't (benign plants), the authors enable practitioners to make informed deployment decisions. This represents a maturing perspective on AI in engineering: not magic solutions, but specialized tools with well-understood boundaries.

• →Open-source LLMs excel as structural reasoning tools for coupled MIMO control systems but offer no advantage for simple loops
• →Sample efficiency advantage of ~6x over global optimizers grows with plant dimension, making LLMs valuable for expensive-to-evaluate real systems
• →LLM-guided control structures remain interpretable and transferable across optimization methods, unlike pure black-box optimization
• →Performance scales across four different open models, suggesting the approach generalizes beyond specific architectures
• →Benchmark establishes clear boundaries for LLM utility in control engineering, advancing beyond hype-driven claims