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. Our approach involves developing a digital twin system that contains descriptive information about the production and retrofitting the automation system to offer unified interfaces of fine-granular functionalities or skills executable by automation components or modules. Subsequently, LLM-Agents are designed to interpret descriptive information in the digital twins and control the physical system through RESTful interfaces. These LLM-Agents serve as intelligent agents 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 also underscoring the critical insights and limitations for future work.

中文翻译：

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走向自治系统：使用大型语言模型代理增强的灵活模块化生产系统

在本文中，我们提出了一个结合大型语言模型 (LLM)、数字孪生和工业自动化系统的新颖框架，以实现生产过程的智能规划和控制。我们的方法涉及开发一个数字孪生系统，其中包含有关生产的描述性信息，并改造自动化系统以提供由自动化组件或模块执行的细粒度功能或技能的统一接口。随后，LLM-Agents 旨在解释数字孪生中的描述性信息，并通过 RESTful 接口控制物理系统。这些 LLM-Agent 作为自动化系统中的智能代理，能够自主规划和控制灵活的生产。给定任务指令作为输入，LLM-代理协调一系列原子功能和技能来完成任务。我们演示了我们实施的原型如何处理未预定义的任务、计划生产过程并执行操作。这项研究强调了将 LLM 集成到工业自动化系统中以实现更敏捷、灵活和适应性更强的生产过程的潜力，同时也强调了未来工作的关键见解和局限性。