This paper examines the path-tracking control issue for tracked mobile robots (TMRs) operating in complex terrains, focusing on improving their autonomous operation capabilities. Considering the system’s complex dynamic model, environmental uncertainties, and non-linear characteristics, especially the phenomenon of track slippage, a dynamic model that incorporates track slippage is proposed. A sliding factor observer is then designed to estimate slippage parameters, ensuring the control system remains stable and accurate despite uncertainties. A hierarchical control architecture is introduced, with the upper-level controller using a kinematic model to generate desired rotational speed commands for the left and right drive wheels. The lower-level controller, operating on a dynamic model, adjusts motor torque to achieve these desired speeds. Utilizing sliding mode control strategies, combined with adaptive laws and nonlinear control methods, the controller effectively addresses the issue of high-frequency chattering arising from the use of signum functions, thereby enhancing the lifespan of actuators and overall system control performance. A comprehensive simulation and experimental setup for real TMR systems is established to validate the proposed control strategy. Results demonstrate that the control scheme effectively achieves trajectory tracking across various unstructured terrains, exhibiting strong robustness and stability.

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.

The performance of pneumatic control valves, characterized by speed, stability, and accuracy, is critical for industrial production and energy efficiency. Traditional PID and fuzzy control methods face limitations in achieving high-precision control due to structural constraints. This study proposes a fractional-order proportional-integral-derivative (FOPID) controller optimized for pneumatic control valves, incorporating a novel overshoot-penalty objective function. To enhance optimization, the Hippopotamus Optimization (HO) is improved with Genetic Algorithm (GA). Simulation and experimental results demonstrate the proposed GAHO-based FOPID controller achieves a settling time of 5.25 s and an overshoot of 0.88%, significantly surpassing conventional methods. These results establish that the proposed FOPID controller as an effective solution for improving the stability, efficiency, and safety of pneumatic control systems in industrial applications.

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.

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach.

In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment.