Control strategy for an intelligent shearer height adjusting system

被引：0

作者：

Fan Q. ^{[1
]}

Li W. ^{[1
]}

Wang Y. ^{[1
]}

Zhou L. ^{[1
]}

Yang X. ^{[1
]}

Ye G. ^{[1
]}

机构：

[1] School of Mechanical and Electrical Engineering, China University of Mining and Technology

来源：

Mining Science and Technology | 2010年 / 20卷 / 06期

基金：

国家高技术研究发展计划(863计划);

关键词：

Dynamic fuzzy neural network; Height adjusting system; Shearer;

D O I：

10.1016/S1674-5264(09)60305-7

中图分类号：

学科分类号：

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 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. © 2010 China University of Mining and Technology.

引用

页码：908 / 912

页数：4

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