Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins

2025-10-27

AI Summary

This study addresses the joint optimization of accuracy, adaptability, and interpretability in dynamic system modeling and control for digital twins. We propose a Hybrid Analytical Modeling (HAM) framework that synergistically integrates first-principles physics with data-driven learning, and—novelty—we embed large language models (LLMs) into the digital twin closed loop to enable interpretable human–AI collaborative decision-making. We comparatively evaluate model predictive control (MPC), deep reinforcement learning (RL), and LLM-based control on a micro-greenhouse testbed. Results show that HAM achieves the optimal trade-off among modeling accuracy, generalization capability, and computational efficiency; MPC exhibits superior robustness; RL demonstrates exceptional sim-to-real transfer adaptability; and the LLM-based controller significantly enhances control policy flexibility and interactive interpretability. This work advances the integration of mechanistic understanding, statistical learning, and linguistic reasoning in cyber-physical system control.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Hybrid modeling balances accuracy and computational efficiency
• Reinforcement learning enables adaptable control in digital twins
• Large language models facilitate human-AI interaction for control

Innovation

• Hybrid modeling balances accuracy and computational efficiency
• Reinforcement learning enables adaptable control strategies
• Large language models facilitate human-AI interaction control