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.

翻译：本文提出一种融合大语言模型（LLMs）、数字孪生与工业自动化系统的新型框架，实现生产过程的智能规划与控制。我们对模块化生产设施的自动化系统进行改造，创建了细粒度功能与粗粒度技能的自动化控制接口。低层级功能由自动化组件执行，高层级技能则由自动化模块完成。随后开发数字孪生系统，注册这些接口并包含生产系统的附加描述信息。基于改造后的自动化系统与构建的数字孪生，设计了大语言模型智能体（LLM-agents），使其能够解析数字孪生中的描述信息并通过服务接口控制物理系统。这些LLM智能体作为自动化系统不同层级的智能体，实现灵活生产的自主规划与控制。给定任务指令作为输入，LLM智能体编排原子功能与技能序列以完成任务。我们展示了实现的原型系统如何处理未提前定义的任务、规划生产流程并执行操作。本研究揭示了将大语言模型集成至工业自动化系统以实现更敏捷、灵活与自适应生产过程的潜力，同时也为未来工作提供了关键见解与局限性的分析。