An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

2026-07-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A structured large-language-model-driven workflow automates multi-variable control design from dynamic process models. This workflow decomposes the design task into constrained code-generation steps, including plant-interface construction, normalization, MV-CV pairing, controller specification, closed loop simulation, scenario generation, performance evaluation, and Bayesian-optimization (BO) based tuning. Generated artifacts are executed and validated, with failed artifacts repaired using feedback. Demonstrated on a nonlinear gas-preheater benchmark, the approach produced a physically consistent decentralized PI feedback-feedforward control structure and an executable tuning environment. Bayesian optimization reduced the closed loop performance objective, aggregating set-point tracking and disturbance-rejection errors, by approximately 26.5% relative to the initial controller, primarily through improved pressure-loop transient performance.

Key takeaway

For control engineers or AI scientists tasked with designing and optimizing complex process control systems, this LLM-driven workflow offers a promising path to automation. You can leverage structured code generation to accelerate the development of control strategies and tuning environments. Consider integrating similar validation-feedback loops to automatically repair generated control artifacts, potentially reducing manual iteration and improving system performance, as demonstrated by the 26.5% objective reduction.

Key insights

LLMs can automate multi-variable control design and tuning through structured code generation and validation.

Principles

• Control design tasks can be decomposed into constrained code-generation steps.
• Validation feedback is crucial for repairing failed generated artifacts.

Method

The workflow involves plant-interface construction, normalization, MV-CV pairing, controller specification, closed loop simulation, scenario generation, performance evaluation, and Bayesian optimization for tuning, with validation and repair.

In practice

• Generates decentralized PI feedback-feedforward control structures.
• Enables executable tuning environments for process control.

Topics

• LLM-driven Workflow
• Automated Process Control
• Multi-variable Control
• Dynamic Process Models
• Bayesian Optimization
• Code Generation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.