🤖 AI Summary
This work addresses the limitations of traditional industrial automation systems, which rely on fixed rules and struggle to autonomously interpret tasks or adapt in dynamic environments. The authors propose a novel three-layer framework that integrates large language models (LLMs) with digital twins, employing TPSR-based task modeling to translate natural language instructions into executable workflows. Four distinct LLM agent roles are designed to enable goal-directed, adaptive behavior. This study presents the first systematic integration of LLM agents and digital twins, endowing industrial systems with generalized reasoning and autonomous decision-making capabilities to support dynamic task planning and human–machine collaboration. Prototype experiments demonstrate high task executability, instruction fidelity, and generation accuracy, significantly reducing manual intervention while enhancing system adaptability and usability.
📝 Abstract
Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human-machine interaction. However, traditional rule-based systems rely on fixed logic and cannot autonomously adapt to changing conditions. Consequently, current automation systems lack a systematic approach for integrating adaptive and generalizable reasoning capabilities for interpreting, planning, and executing user tasks across dynamic environments and heterogeneous components.
This dissertation proposes a three-layer framework that integrates large language models (LLMs), digital twins, and automation systems into an autonomous system. Autonomy is defined as a design property assigned to system components and enabled through LLM-based reasoning to achieve adaptive, goal-oriented behavior. The Task-Process-Service-Resource (TPSR) model is introduced to transform user tasks into executable processes. Four LLM roles are identified: process orchestration, service matching, digital resource generation, and agent-as-a-service. Five peer-reviewed studies develop and refine these concepts using the design science research methodology.
Case studies and prototypes demonstrate adaptive task planning, event-driven control, simulation-based parameterization, and digital model generation. Results show high task executability, command correctness, and content-generation accuracy while reducing manual effort. The framework enables the integration of LLM-based reasoning into industrial automation systems and improves adaptability and usability. Limitations include dependence on accurate digital representations, the computational demands of LLMs, and the need for human intervention in safety-critical situations.