Abstract
At present, the positioning control of the hydraulic support pushing systems in fully mechanized mining faces uses an electrohydraulic directional valve as the control component, while the current research mainly focuses on servo valves, proportional valves, high−speed on−off valves, and electromagnetic directional valves. At present, the positioning control for electrohydraulic directional valves is only a simple logical control. Therefore, in order to improve the positioning control accuracy of the hydraulic support pushing system, a predictive positioning control strategy based on iterative learning was designed. Firstly, mathematical modeling of the hydraulic support pulling process was carried out, and its state−space equation was established. Secondly, an iterative learning controller with a state observer was designed, in which the iterative learning method was used to predict the control advance in the positioning process, and the state observer was used to estimate the parameters that could not be measured by the system, so as to improve the control accuracy in the broaching process. Then, a SimulationX–Simulink joint simulation model of the position control system of a multi−cylinder pulling hydraulic support was built, and the designed iterative learning controller was compared with the BP neural network controller. Finally, a test platform for the hydraulic support pushing system was built, and the proposed control strategy was experimentally verified. The research results show that the iterative learning control strategy proposed for the electrohydraulic directional valve not only simplifies the design process of the controller but also has higher positioning control accuracy. The single−cylinder positioning control accuracy can be controlled within 10 mm, and the multi−cylinder coordinated positioning control accuracy can be controlled within 15 mm, which meets the accuracy requirements of the site.
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