An Intelligent Distributed Adaptive Control Method for Multi-Channel Thermal Regulation in Fire-Resistance Testing Equipment
Accurate regulation of combustion temperature is critical for objectively evaluating the fire-resistance performance of cables. However, existing temperature control strategies mainly rely on centralized regulation methods, which struggle to simultaneously address the nonlinear coupling among multiple heat sources, spatial thermal non-uniformity, and dynamic temperature fluctuations. To address these challenges, a multi-channel self-adaptive temperature control method based on distributed optimization and computational modeling is proposed in this study. First, a data-driven computational model based on an attention-enhanced multi-channel convolutional neural network is developed to characterize the complex nonlinear relationship between distributed heat inputs and the resulting temperature field, enabling accurate thermal state perception and prediction. Subsequently, a data-driven NSGA-III optimization algorithm is introduced to achieve dynamic allocation and coordinated optimization of heat flux among multiple independent heating channels. Furthermore, a deep reinforcement learning-based adaptive decision framework is established to realize autonomous adjustment of heating strategies under varying testing conditions. The proposed framework integrates thermal modeling, distributed optimization, and intelligent decision-making to achieve real-time adaptive control of multi-source heating systems. Experimental validation on practical fire-resistance testing equipment demonstrates that the proposed framework achieves an R2 of 0.9745 with an MAE of 19.90 °C in the closed-loop control evaluation and provides improved spatial thermal uniformity compared with conventional control strategies.