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.

翻译：本文提出了一种融合大语言模型、数字孪生与工业自动化系统的创新框架，旨在实现生产流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，构建了细粒度功能与粗粒度技能的标准化控制接口。底层功能由自动化组件执行，高层技能则由自动化模块完成。进而开发数字孪生系统，注册上述接口并集成生产系统的补充描述信息。基于改造后的自动化系统与数字孪生体，我们设计了大语言模型代理，使其能够解析数字孪生中的描述性信息，并通过服务接口控制物理系统。这些代理作为自动化系统中不同层级的智能体，实现了灵活生产的自主规划与控制。当输入任务指令后，大语言模型代理可编排原子化功能与技能序列以完成任务。我们通过原型系统验证了其处理未预定义任务、规划生产流程并执行操作的能力。本研究揭示了在智能工厂背景下，将大语言模型集成至工业自动化系统对实现更敏捷、灵活与自适应生产流程的潜力，同时亦指出了未来研究的关键洞见与局限性。