Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

The formation of a coal mine roadway cross-section is a primary task of the boom-type roadheader. This paper proposes an intelligent robust control scheme for the cutting head trajectory of a coal mine tunneling robot, which is susceptible to unknown external disturbances, system nonlinearity, and parameter uncertainties. First, the working conditions of the cutting section were analyzed, and a mathematical model was established. Then, a high-gain disturbance observer was designed based on the system model to analyze cutting loads and compensate for uncertainties and disturbances. A sliding mode controller was proposed using the backstepping design method, incorporating a saturation function control term to avoid chattering. The eel foraging optimization algorithm was also improved and used to tune the controller parameters. A simulation model of the system was developed for performance comparison tests. Finally, experimental verification was conducted under actual working conditions in a tunnel face, and the results demonstrated the effectiveness of the proposed control method.

High-speed on / off valves (HSVs) are characterized by their simple structure, high reliability, and insensitivity to contamination. However, the driving power and flow constraints limit their application in the fully mechanized mining face control system. To apply HSVs in the mining face, a novel two-stage structure for the HSV, incorporating an internal feedback channel, is proposed in this study. This design allows control with meager driving power, rapid response, and a larger flow capacity. A state-space modeling approach is employed in this study to analyze the dynamic response of the internal poppet spool. Additionally, an equivalent magnetic circuit model is developed for the driving electromagnetic solenoid, and the solenoid’s mathematical modeling is verified through finite element simulations and experiments. Utilizing mathematical models and considering response time and flow rate as objective functions, the electromagnetic solenoid and valve structures are optimized using the electric eel foraging optimization algorithm. The final design specifications are obtained by further analyzing the optimized design parameters. A prototype is manufactured and subjected to experimental validation. The experimental results demonstrate that the proposed structure achieves an opening time of 5 ms, a closing time of 5 ms, and a flow rate of 20 L/min.

In high-pressure pulsatile pipeline transport of high-concentration viscous paste, severe backflow is generated when there are sudden pressure drops due to the paste's elastic recovery, resulting in strong impact and damage to the pipeline system. The paste demonstrates high viscosity, yield stress, compressibility, and shear elasticity. Nevertheless, the effect of the paste's elastoviscoplasticity properties on unsteady flow during pipeline transport remains uncertain. Consequently, we derived the unsteady flow control equations of the paste that accounts for the elastoviscoplasticity behavior and linear compressibility. We conducted experimental research by building a circulating pipeline transportation system and performed numerical analysis using the finite-volume method (modified RheoFoam solver). The results demonstrate a substantial increase in pressure fluctuations within the pipeline during rapid pressure drops due to the elasticity of the paste. When accounting for elasticity, the maximum backflow velocity increases by 32.4% compared to cases where elasticity is not considered. The backflow velocity during piston pump reversal is influenced by the pressure level and the rate of pressure drop. Pressure fluctuations are more likely to propagate along the pipeline during high-pressure than during low-pressure periods. These findings offer guidance for analyzing unsteady flow characteristics in paste pipeline transport and designing pipeline systems.

The design of the high-speed on/off valve is challenging due to the interrelated structural parameters of its driving actuator. Hence, this study proposes a multi-objective optimization approach that integrates a backpropagation neural network and artificial fish swarm algorithm optimization techniques to accurately model the electromagnetic solenoid structure. The backpropagation neural network is fitted and trained using simulation data to obtain a reduced-order model of the system, enabling the precise prediction of the system’s output based on the input structural parameters. By employing the artificial fish swarm algorithms, with optimization objectives focusing on the valve’s opening and closing times, a Pareto optimal solution set comprising 30 solutions is generated. Utilizing the optimized structural parameters, a prototype is manufactured and an experimental setup is constructed to verify the dynamic characteristics and flow pressure drop. The high-speed on/off valve achieves an approximate opening and closing time of 3 ms. Notably, the system output predicted using the backpropagation neural network (BPNN) exhibits consistency with the experimental findings, providing a reliable alternative to mathematical modeling.

This paper proposes a temporary support system for improving the efficiency and safety of underground roadway excavation in coal mines. Firstly, this study establishes a calculation model for the initial supporting force of the excavation of roadway temporary support and a gray system-based automatic prediction model for the initial supporting force level, based on the mechanism of temporary support controlling the roof. These models enable the prediction of the required initial supporting force at different locations along the roadway’s temporary support area, thereby providing a basis for controlling the initial supporting force of the temporary support system. To achieve efficient and adaptive control of the initial supporting force of temporary supports at different locations, this study designs a support force controller based on Simulated Annealing Particle Swarm Optimization Proportional-Integral-Derivative (SAPSO-PID). This study establishes a mathematical model for the hydraulic cylinder pressure system controlled by the temporary support overflow valve and conducts a stability analysis and model verification. The study constructs a simulation control system for the initial supporting force based on SAPSO-PID using the combined simulation platform of AMESim and Matlab. The simulation results demonstrate that the proposed support force control system efficiently achieves adaptive control of the initial supporting force of temporary supports. An experimental system in the underground roadway of a coal mine is constructed to validate the results of the simulation analysis.