With the advancement of smart education and carbon neutrality goals, optimizing Indoor Environmental Quality (IEQ) while minimizing energy consumption is critical. Traditional PID or rule-based strategies struggle with the strong non-linearity and time delays of photothermal coupling in high-density classrooms. This paper proposes an adaptive closed-loop control framework fusing Digital Twin (DT) and Deep Reinforcement Learning (DRL). A high-fidelity multi-physics model is constructed as a virtual testbed, utilizing the Proximal Policy Optimization (PPO) algorithm to learn multi-objective strategies. The trained agent is deployed to an edge gateway for real-time inference. Experimental results from a field study distinguish this work from pure simulations. Results demonstrate that compared to PID baselines, the proposed strategy reduces energy consumption by 28.4% while maintaining thermal comfort (PMV) and visual comfort compliance. Furthermore, the variance of PMV is reduced by 66.7%, and system recovery time under stochastic disturbances is shortened by 31.4%.
As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduce…
Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep learning, and traffic simulati…
In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it construc…
Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programm…
Deep reinforcement learning (DRL) has shown potential for robotic training in virtual environments; however, challenges remain in bridging simulation and real-world deployment. This paper introduces an extended reinforcement learning framework that advances beyond traditional sin…
Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces…