An LLM-Driven Workflow for Automated Process Contro... | AI Research

An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
This research introduces an automated, agentic workflow that uses large language models (LLMs) to design and tune industrial control systems. Traditionally, converting a dynamic process model into a functional control system—including selecting loop pairings, writing controller code, and tuning parameters—is a manual, expert-driven process. This framework automates these engineering steps, creating a self-contained pipeline that can generate, validate, and optimize control strategies for modular process systems without human intervention.

How the Workflow Works

The system decomposes the complex task of control design into a series of constrained, sequential code-generation steps. These include constructing a plant interface, normalizing variables, determining how to pair inputs and outputs (MV-CV pairing), specifying the controller, and building a simulation environment.
A critical feature of this approach is the "validation and repair" loop. After the LLM generates code for each step, the system automatically checks it for syntax and interface errors. If a piece of code fails, the system provides the error feedback to the LLM to trigger a repair. This ensures that only functional, validated artifacts are passed to the next stage of the design process.

Building the Tuning Environment

Beyond just writing the controller code, the workflow automatically constructs an executable environment to refine the system's performance. It uses the dynamic model to estimate settling times and generates a standardized evaluation scenario. This scenario is then used by a Bayesian-optimization (BO) routine to fine-tune the controller’s proportional-integral (PI) parameters. By using a fixed simulation path and scenario, the system ensures that the optimization process is consistent and objective-driven.

Performance and Results

The researchers tested the framework on a nonlinear gas-preheater benchmark, which involves complex, coupled pressure and temperature dynamics. The workflow successfully generated a physically consistent decentralized PI feedback-feedforward control structure. Following the automated tuning phase, the Bayesian-optimization process reduced the closed-loop performance objective—which measures set-point tracking and disturbance rejection—by approximately 26.5% compared to the initial controller generated by the workflow.

Considerations for Future Development

While the results demonstrate that LLMs can effectively construct executable control-design workflows, the authors note that this is a proof-of-concept. The current framework was tested on a specific, smaller-scale benchmark. Future work is needed to validate the approach on larger, more complex plantwide-control systems to ensure it remains robust and reliable when applied to broader industrial configurations.