Intelligent decision method for the position and attitude self-adjustment of hydraulic support groups driven by a digital twin system

Abstract

The hydraulic support group is often skewed during the propulsion process, but the existing technology cannot autonomously adjust its position and attitude according to the actual working conditions. This paper proposes an intelligent decision method for the position and attitude self-adjustment of hydraulic support groups driven by a digital twin system. The method reconstructs the position and attitude of the hydraulic support, completes virtual decision-making, virtual execution and strategy optimization for the adjustment behavior in the virtual space, and finally derives the optimal control command to reversely control the real support to achieve the self-adjustment of the position and attitude of the support. First, the full position and attitude parameter matrix and the 3D workspace are defined to describe the position and attitude of the hydraulic support, and a pose analysis method of the hydraulic support group is proposed based on the lengths of the column and balance jack. Second, the method of predicting the ideal pose of the support, the synergistic method of the adjustment device, and the method for the calculation of its elongation are studied. Finally, a digital twin system facing physical support prototypes was built in a laboratory environment, and three key experiments were carried out for the virtual reconstruction of the support pose, the intelligent decision-making and virtual execution of the adjustment behavior, and the autonomous adjustment of the physical prototype based on virtual decision-making results. The experimental results show that virtual decision-making can effectively guide the real adjustment behavior, and the applicability and influencing factors of the adjustment effect are further studied. The proposed method compensates for the vacancy of hydraulic support self-adjustment technology, and can provide a reference for the intelligent control of fully-mechanized mining equipment.