摘要
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 sys- tem real-time performance and reliability. Parameters from a German Eickhoff SL500 shearer were used with Maflab/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.
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 sys-tem 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.
基金
support for this work, provided by the National High Technology Research and Development Program of China (No2008AA062202)
China University of Mining & Technology Scaling Program
