Control strategy for an intelligent shearer height adjusting system

An intelligent shearer height adjusting system is a key technology for mining at a man-less working face. A control strategy for a shearer height adjusting system based on a mathematical model of the height adjusting mechanism is proposed. It considers the non-linearity and time variations in the control process and uses Dynamic Fuzzy Neural Networks (D-FNN). The inverse characteristics of the system are studied. An adaptive on-line learning and error compensation mechanism guarantees system real-time performance and reliability. Parameters from a German Eickhoff SL500 shearer were used with Matlab/Simulink to simulate a height adjusting control system. Simulation shows that the trace error of a D-FNN controller is smaller than that of a PID controller. Also, the D-FNN control scheme has good generalization and tracking performance, which allow it to satisfy the needs of a shearer height adjusting system.