Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins
This research addresses the challenge of optimizing digital twin systems for dynamical control, offering incremental improvements by integrating and comparing existing methods in modeling and control.
This work tackled the problem of modeling and controlling dynamical systems using digital twins, comparing hybrid, physics-based, and data-driven models and AI-driven controllers on a miniature greenhouse test platform, with results showing Hybrid Analysis and Modeling (HAM) provided balanced performance and Model Predictive Control (MPC), Reinforcement Learning (RL), and Large Language Model (LLM)-based controllers each offered distinct trade-offs in precision, adaptability, and interaction.
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.