Title :
Application of RBF hierarchical neural network in automatic horizon control system of memory-cutting shearer
Author :
Su, Xiuping ; Li, Wei ; Zhang, Lili ; Wang, Yuqiao ; Fan, Qigao ; Sun, Chuantang ; Yu, Ling
Author_Institution :
Sch. of Mech. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
The control of hydraulic servo system is the control key to automatic horizon device of memory-cutting shear. The radial basis function (RBF) hierarchical neural network (RBFHNN) is presented to control the hydraulic servo system of automatic horizon device of memory-cutting shearer. The RBFHNN can identify the sensitivity of the hydraulic servo system in real time in the learning phase, and can make the hydraulic horizon device rapidly track the roof curve of the last cycle as feed-forward controller in the control phase. The simulation results for the hydraulic servo system of shearer horizon device show the control system based on the RBFHNN is faster, and has higher accuracy and better stability.
Keywords :
hydraulic control equipment; neurocontrollers; radial basis function networks; sensitivity; servomechanisms; shearing; stability; RBF hierarchical neural network; automatic horizon control system; automatic horizon device; control phase; feed-forward controller; hydraulic horizon device; hydraulic servo system; learning phase; memory-cutting shearer; radial basis function; roof curve; sensitivity; shearer horizon device; stability; Automatic control; Control system synthesis; Control systems; Feedforward neural networks; Mathematical model; Neural networks; Neurons; Real time systems; Servomechanisms; Vectors; Automatic Horizon Control; Hierarchical Neural Networks; Radial Basis Function; Shearer;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498888
