The friction coefficient of rock joints is closely related to the stability of the slope. However, it is difficult to predict the friction coefficient due to the influence of surface roughness and mechanical properties of rocks. In this study, we use a method that combines theoretical analysis with a sandstone sliding friction test and propose a model to predict the friction coefficient of Sandstone Joint. A sandstone sliding friction test was performed on a self-made reciprocating sliding friction test device. Good agreement between the estimated values and test values verified the validity of the friction coefficient prediction model. Through an analysis of the friction coefficient in sandstone sliding, it was established that the larger the wear mass, the larger the friction coefficient in sliding, and the larger the wear area, the smaller the friction coefficient. With the cycles increasing of sandstone, the friction coefficient gradually decreased before finally reaching a stable value. Comparisons between the estimated value and test results showed that when the wear difference coefficient c = 2.0 and the meshing friction amplification coefficient K = 1.4, the minimum error was 2.89%. The results obtained are significant in the control of slope sliding.

The cutting head is the core working mechanism of the roadheader for coal-rock materials cutting. The efficient and high performance design of cutting head is the key to improve the road head digging and mining technology. In this paper, based on cutting head design theory and virtual prototype technology, we propose a computer-aided structure design and performance optimization method for cutting head. We compile the calculation code and realize the reading and storing of relevant data through Excel. In particular, to obtain more realistic cutting performance data of the cutting head, we construct a coupling model of cutting head cutting rock wall based on virtual prototype technology, and then establish a database matching structural parameters, working parameters, coal-rock properties and cutting performance through extensive simulations. Based on the method, we complete the design of EBZ220 roadheader cutting head. We show that our method can realize the fast and efficient design of cutting head, and the designed cutting head has good working performance.

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Aiming to solve the problems of poor dynamic response characteristics and the weak anti-jamming capability of the conventional proportional–integral–derivative (PID) controlled pump-motor servo system (PMSS) under the actual working environment, this study created a brand new hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithm to determine the best parameters of the PID controller for the PMSS speed control to make the PMSS achieve a constant speed control. We developed a GWOPSO-PID controller and compared it with a conventional PID controller, GWO-PID, PSO-PID, and GA-PID. In comparison to the other four control methods, the simulation and experimental results demonstrate that the designed GWOPSO-PID control had better dynamic response characteristics, with its rise times being reduced by 78.6%, 64.7%, 67.1%, and 41.5%, respectively. Additionally, the system under the GWOPSO-PID control exhibits a good stability and robustness even in the face of different load circumstances, with decreases in the re-equilibration times of 59.6%, 23.4%, 53.2%, and 41.9%, respectively, with a significantly improved immunity to disturbances.

The nonlinear factors in the digital hydraulic cylinder will reduce the accuracy of the control system. In order to improve the control accuracy of the control system, in this paper, a model reference adaptive disturbance rejection control method based on neural network is proposed. Firstly, the dead zone model is used to replace the nonlinear link in the feedback mechanism. A detailed mathematical model of digital hydraulic cylinder is established and the nonlinear hydraulic spring force is also considered, and a complete nonlinear state space model of digital hydraulic cylinder is derived based on LuGre friction model. Then the reference model is designed. By introducing ESO (extended state observer), the uncertainties and external disturbances of the controlled object are all equivalent to a total disturbance. The RBF (Radial Basis Function) network is used to approximate the unknown function FZ, the neural model reference adaptive disturbance rejection composite controller is designed by using Lyapunov direct method and Barbalat lemma. Finally, the simulation verification is carried out by using MATLAB. The simulation results show that the control strategy can effectively improve the response characteristics of the system, reduce the steady-state error of the system, and improve the robustness of the system.