Fangmin Jiao, Xinle Wang and Yongliang Yuan
In view of the problems of variable working conditions under dynamic load conditions, low efficiency of traditional manual operation, and high energy consumption of equipment in bucket wheel stacker-reclaimers (BWRs), this paper proposes an intelligent control system design scheme based on multi-source data fusion. The system adopts a three-tier architecture design, integrating multi-source sensor data such as 3D laser scanning, RFID positioning calibration, and absolute value encoders to construct a full-time dynamic 3D digital twin model. Based on this, an improved repulsion model path planning algorithm is designed, effectively solving the problems of path oscillation and local optimum traps in dense obstacle environments. At the same time, fuzzy adaptive PID and PSO-GSA hybrid optimization strategies are adopted to achieve precise collaborative control of multiple motors. Experimental results show that the improved repulsion model algorithm achieves a path planning success rate of 94.7% under complex working conditions; the speed tracking difference of multiple motors is controlled within 3%; equipment energy consumption is reduced by 20% compared to before optimization; and the fluctuation range of reclaimer flow rate is controlled at ±1.9%.
Bucket Wheel Reclaimer, Multi-Source Data, Intelligent Control, Collaborative Control