Control Industrial Automation System with Large Language Model Agents
Yuchen Xia, Nasser Jazdi, Jize Zhang, Chaitanya Shah, Michael Weyrich
Abstract
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs’ application in industrial automation settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework is an agent system designed for industrial automation tasks. A structured prompting method and an event-driven modeling mechanism provide the information for LLMs to perform reasoning on different context levels, allowing them to semantically interpret the information, generate production plans, and control operations on the automation system. Furthermore, this framework facilitates the creation of structured datasets for fine-tuning LLMs on this specific downstream application. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, allowing intuitive system operation and configuration through natural language. Demo videos and detailed evaluation data are accessible on GitHub: https://github.com/YuchenXia/LLM4IAS.