Control Industrial Automation System with Large Language Models

2024-09-26

arXiv.org

AI Summary

Traditional industrial automation systems suffer from high operational complexity and require labor-intensive reprogramming for process changes. Method: This paper proposes the first end-to-end large language model (LLM)-driven industrial control system. We introduce an industrial task agent framework integrating structured prompt engineering, multi-semantical-level event-driven modeling, and real-time data integration. Additionally, we propose a reusable, task-specific dataset generation methodology to support LLM domain adaptation and evaluation. Contribution/Results: We establish the first structured prompt paradigm for industrial automation; enable natural-language instruction parsing, dynamic production planning, and closed-loop device control; and support responsive adaptation to unforeseen operational conditions—substantially lowering the human operator barrier. A formal system design and proof-of-concept implementation have been completed, with source code and demonstration videos publicly released.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Prompt Engineering / Prompting
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 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 are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. 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, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS

Problem

• Enabling flexible industrial automation control via LLMs
• Reducing specialized expertise needs for system operation
• Integrating real-time data for adaptive LLM decision-making

Innovation

• Agent system for industrial task control
• Structured prompting method for LLMs
• Event-driven real-time data modeling