In order to solve the problem of unstable fluid supply pressure and serious impact caused by the complicated and changeable working condition of a fully mechanized mining face in coal mines and the sluggish response of the fluid supply system to the fluid demand for the hydraulic support, a control method based on online updating generalized regression neural network (GRNN) was proposed. Firstly, the simulated hydraulic support test platform and co-simulation model were built. Secondly, The optimal flow dataset of steady-pressure fluid supply under different working conditions is calculated by simulation. Furthermore, the GRNN prediction model was established by using dataset and online updating learning technology to predict the optimal fluid supply flow according to environmental parameters. Finally, the optimal flow control method of online updating GRNN was established, and numerical research and experimental verification were also carried out in different working conditions. The results indicated that the proposed control method could track the working conditions of the working face in real time and adjusted the fluid supply flow of the emulsion pump station adaptively, which effectively alleviated the pressure fluctuation and pressure shock, and the system pressure was more stable, meeting the demand of steady-pressure fluid supply on the working face.

In order to solve the problems of frequent pressure fluctuations caused by frequent action of the unloading valve of the pump station and serious hydraulic shock due to the variable amount of fluid used in the hydraulic support system of the coal mining face and the irregularity of the load suffered by the system, a pump–valve cooperative multi-mode stabilizing control method based on a digital unloading valve was proposed. Firstly, a prototype of a digital unloading valve under high-pressure and high water-based conditions was developed, and a digital control scheme was proposed to control the pilot valve by a servo motor to adjust the system pressure in real time. Then, an experimental platform for simulating the hydraulic bracket and a co-simulation model was constructed, and the validity of the co-simulation model was verified through experiments. Secondly, a collaborative multi-mode pressure stabilization control method for the pump valve based on a GRNN (General Regression Neural Network) was established to control the flow and pressure output of the emulsion pumping station according to the actual working conditions. Finally, numerical research and experimental verification were carried out for different working conditions to prove the effectiveness of this method. The results showed that the proposed pressure stabilization control method could adaptively adjust the working state of the digital unloading valve and the liquid supply flow of the emulsion pump station according to the working condition of the hydraulic support, effectively reducing the frequency and amplitude of the system pressure fluctuations and making the system pressure more stable.

To advance the construction of intelligent mining, electro-hydraulic digital control technology has emerged as a critical direction for the digital transformation of mining machinery. This study proposes a digital control scheme based on the pressure state of the system and the operating state of the actuator. The scheme utilises a novel convergence rate sliding film position control method to regulate the system pressure in real time by controlling the pilot valve, which is driven by a permanent magnet synchronous motor (PMSM). Moreover, a prototype of an incremental digital pressure control valve was developed for high-pressure, high water-based working conditions. A simulation model of the valve was established using AMESim/Simulink, and dynamic characteristics under various operating conditions were analyzed. The relative error between simulated and experimental pressure results remained within ±4.7%. Finally, a multi-parameter optimization was conducted using a genetic algorithm. The results demonstrate that the optimized digital pressure control valve achieved a stabilized inlet pressure within 44.8 ms, with a pressure overshoot of 4.1% and a response time of 20.1 ms, exhibiting excellent real-time dynamic pressure regulation capabilities. This study provides a theoretical foundation and practical reference for comprehensive research on pressure control in underground emulsion pump stations.