融合物理模型与数据驱动，用数字孪生提升系统建模与控制精度。
Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins
- 混合建模结合物理规律与数据学习，兼顾准确性与泛化能力。
- 混合模型在预测任务中表现最佳，长短期记忆网络精度高但耗资源。
- 大语言模型控制适合人机协作，强化学习适应性强，模型预测控制最稳定。
本研究探讨数字孪生在动态系统建模与控制中的应用，整合基于物理、数据驱动及混合方法，并对比传统与人工智能驱动的控制器。以微型温室为实验平台，构建四类预测模型：线性模型、物理基础建模（PBM）、长短期记忆网络（LSTM）和混合分析与建模（HAM），在插值与外推场景下进行比较。同时实现三种控制策略：模型预测控制（MPC）、强化学习（RL）和基于大语言模型（LLM）的控制，评估其在精度、适应性与实施成本间的权衡。结果表明，在建模方面，HAM在准确性、泛化能力和计算效率之间表现最均衡；LSTM虽精度高但资源消耗大。在控制方面，MPC表现出鲁棒且可预测的性能，RL具有强适应性，而结合预测工具的LLM控制器能实现灵活的人机交互。
原文摘要 · Abstract (English)
This work investigates the use of digital twins for dynamical system modeling and control, integrating physics-based, data-driven, and hybrid approaches with both traditional and AI-driven controllers. Using a miniature greenhouse as a test platform, four predictive models Linear, Physics-Based Modeling (PBM), Long Short Term Memory (LSTM), and Hybrid Analysis and Modeling (HAM) are developed and compared under interpolation and extrapolation scenarios. Three control strategies Model Predictive Control (MPC), Reinforcement Learning (RL), and Large Language Model (LLM) based control are also implemented to assess trade-offs in precision, adaptability, and implementation effort. Results show that in modeling HAM provides the most balanced performance across accuracy, generalization, and computational efficiency, while LSTM achieves high precision at greater resource cost. Among controllers, MPC delivers robust and predictable performance, RL demonstrates strong adaptability, and LLM-based controllers offer flexible human-AI interaction when coupled with predictive tools.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。