Can LLMs manage industrial control autonomously?
Autonomous Industrial Control using an Agentic Framework with Large Language Models
This paper proposes a framework for autonomous industrial control using multiple LLM-based agents, particularly for handling unexpected events. A key innovation is the "reprompting architecture," where a "Reprompter Agent" refines the actions of an "Actor Agent" based on feedback from a "Validator Agent" interacting with a digital twin. This iterative feedback loop improves the LLM agent's decision-making, leading to safer and more effective actions. A temperature control case study using different OpenAI LLMs demonstrates the framework's ability to enhance control performance and reliability, especially with reprompting. The research highlights the potential of LLM-based multi-agent systems and reprompting for robust industrial automation.