Robust Closed-Loop Control of Industrial Systems Based on Cloud–Edge Device Collaboration

Under China’s dual-carbon strategic goals, large-scale public centralized heating plays a critical role in energy conservation. However, traditional manual open-loop control suffers from high response latency. Furthermore, existing unidirectional predictive methods lack dynamic feedback correction mechanisms. To address these issues, this study proposes an intelligent dual-system collaborative control architecture specifically designed for public heating systems. This architecture utilizes a cloud–edge device framework. It establishes a two-way linkage between heating equipment and the cloud decision platform. Consequently, it constructs an integrated regulation framework encompassing forward decision generation, reverse state verification, and dynamic feedback correction. Specifically, the forward module utilizes a Mamba-structured state-space model to generate data-driven boiler operation strategies. Meanwhile, the reverse module employs a Temporal Convolutional Network with Monte Carlo Dropout (TCN-MC Dropout). This probabilistic network enables state inversion evaluation with reliable uncertainty prediction intervals. These two modules are deeply coupled through an adaptive feedback correction mechanism. Together, they significantly improve system stability and operational robustness under complex thermal disturbances. Specifically, the proposed architecture achieves a room temperature compliance rate exceeding 96% and restricts temperature fluctuations to within ±0.75 °C. Simultaneously, it reduces boiler energy consumption by over 16.4%. This solution has been successfully deployed in the heating network at Shaanxi Normal University as a representative real-world case study. Ultimately, it provides a practical technical reference for the intelligent upgrading and low-carbon transformation of public centralized heating systems.