  In this paper, we present a novel framework that combines large language
models (LLMs), digital twins and industrial automation system to enable
intelligent planning and control of production processes. We retrofit the
automation system for a modular production facility and create executable
control interfaces of fine-granular functionalities and coarse-granular skills.
Low-level functionalities are executed by automation components, and high-level
skills are performed by automation modules. Subsequently, a digital twin system
is developed, registering these interfaces and containing additional
descriptive information about the production system. Based on the retrofitted
automation system and the created digital twins, LLM-agents are designed to
interpret descriptive information in the digital twins and control the physical
system through service interfaces. These LLM-agents serve as intelligent agents
on different levels within an automation system, enabling autonomous planning
and control of flexible production. Given a task instruction as input, the
LLM-agents orchestrate a sequence of atomic functionalities and skills to
accomplish the task. We demonstrate how our implemented prototype can handle
un-predefined tasks, plan a production process, and execute the operations.
This research highlights the potential of integrating LLMs into industrial
automation systems in the context of smart factory for more agile, flexible,
and adaptive production processes, while it also underscores the critical
insights and limitations for future work. Demos at:
https://github.com/YuchenXia/GPT4IndustrialAutomation
