Research on Head Deviation Control Method of Finishing Strip Based on Deep Reinforcement Learning

During hot‐strip production, lateral head off‐tracking at the finisher exit degrades strip quality and destabilizes line operation, forming a key bottleneck to further improvements in flatness accuracy and unmanned operation. To address the issues of response latency, tuning complexity, and limited adaptability inherent in manual control, this study proposes a deep reinforcement learning (DRL)‐based method for head deviation control in finishing rolling. Targeting higher control accuracy and system stability, we first define a process‐parameter framework governing off‐tracking and select representative variables—strip width and thickness, interstand rolling‐force asymmetry, work‐roll‐bending force, flow‐stress coefficient, and historical deviation records. On this basis, a particle swarm optimization–support vector regression (PSO–SVR) model is employed to predict deviation, providing feedforward information to the controller. We then introduce a deep Q‐network (DQN), formulated under a Markov Decision Process (MDP), to learn the control policy; closed‐loop regulation is achieved by adjusting key actuator setpoints (e.g., preset gap differential). The method was deployed and tested on an industrial 2250 mm hot‐strip mill under real production conditions. Across multiple steel grades, it reduced head deviation by about 30% on average, improving flatness quality and operational stability. These results provide a practical basis and engineering reference for intelligent control of strip rolling and indicate strong prospects for broad industrial adoption.