Research on the Supporting Dynamics and Adaptive Intelligent Control Method for Hydraulic Support

The intelligent level of hydraulic support directly impacts the safe mining capacity and efficiency of the entire fully mechanized mining face. Adaptive intelligent control of its supporting pose, to adapt to the complex and ever-changing geological conditions of coal seams and variations in roof characteristics, is crucial for intelligent mining. This paper proposes and validates a novel adaptive adjustment method for the supporting pose, utilizing hydraulic support dynamics and transfer reinforcement learning. First, the supporting dynamics based on the coupling relationship between the hydraulic support and the surrounding rock of the coal seam are analyzed, and a supporting reinforcement learning model based on the Markov Decision Process is designed. Based on this model, a gradient-optimized Proximal Policy Optimization method is proposed, and a virtual dynamic simulator is built for training a supporting pose control policy. To transfer the virtually trained strategy to real hydraulic supports for practical application and to bridge the gap between support strategy simulation and reality, a progressive neural network architecture is introduced to mitigate the execution gap of support strategies under real-world conditions. Experimental results demonstrate that the proposed method can effectively and autonomously adjust the supporting pose to adapt to changes in the complex and variable coal seam roof. Furthermore, this work provides a theoretical foundation and practical engineering application for the development of intelligent support robots in coal mines.