Hang Chen

Considering the complexity and difficulty of obtaining certain parameters in the electro-hydraulic proportional control system, a precise transfer function of the system was derived through parameter identification using experimental data obtained from an Amesim simulation model after establishing a basic mathematical model. This approach reduces the reliance on accurate parameters of individual components. A feedforward-feedback composite controller was designed, and its effectiveness was validated in Simulink using the system’s transfer function. Subsequently, the dead zone range of the proportional valve was determined through experiments, and a dead zone compensation strategy was designed, which reduced the time required for the proportional valve to traverse the dead zone by 89.4%. Based on the dead zone compensation, trajectory tracking experiments were conducted to validate the effectiveness of the feedforward-feedback composite controller. Under fixed disturbances, the trajectory tracking error was reduced by 53.8% compared to feedback control. Under time-varying load disturbances, the trajectory tracking error was reduced by 51.2% compared to feedback control.

Coal–rock recognition is a key technology to realize intelligent shearer and a prerequisite for achieving safe and efficient production in coal mining working face. Based on the analysis of the characteristics and defects of the current recognition methods, this article proposes a novel coal–rock recognition method based on laser scanning technology. First, a coal–rock point cloud data simplification method is designed based on feature points preserving. The purpose is to retain abundant information of coal–rock characteristics and simultaneously meet the requirements of simplicity and precision. Then, an improved ant colony optimization (IACO) algorithm is presented by the two strategies to enhance the optimization efficiency and search ability. The strategies are implemented by adaptively adjusting the pheromone volatilization coefficient and the update step of ant colony position. The IACO is combined with the 2-D OTSU (IACO-TOTSU) to determine the optimal intensity threshold of coal–rock point cloud data, which is utilized to consummate the growth rule of the region growing (RG) algorithm. Meanwhile, the initial seed point of RG is optimized through the curvature calculation of each point and an improved region growing algorithm (IACO-TOTSU-RG) is then performed to achieve the segmentation and recognition of coal–rock point cloud data. Experimental test results indicate that the proposed coal–rock recognition method outperforms others and the coal–rock recognition accuracy can reach above 90%. Finally, an industrial application is provided to prove the practicability and feasibility of the proposed method.