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 for more agile, flexible, and adaptive production processes,
while it also underscores the critical insights and limitations for future
work.

本文提出了一个将大型语言模型（LLMs）、数字孪生和工业自动化系统相结合的新型框架，以实现生产过程的智能规划和控制，强调将 LLMs 集成到工业自动化系统中以实现更敏捷、灵活和自适应的生产流程的潜力并指出未来工作的关键见解和限制。