Title :
Neural network based online self-learning adaptive PID control for automatic ranging cutting height of shearer
Author :
Yang, Tie-mei ; Xiong, Shi-Bo
Author_Institution :
Res. Inst. of Mechano-Electron. Eng., Taiyuan Univ. of Technol., Shanxi, China
Abstract :
Concerns the automatic control of the cutting height of a drum-type shearer in coal-mining. We separately use a simple proportional-integral-derivate (PID) controller and a neural network based online self-learning adaptive PID control to adjust the cutting height of shearer, the results of the simulation show that the neural network based online self-learning adaptive PID control makes better dynamic property and strong robustness.
Keywords :
adaptive control; computerised control; cutting; learning (artificial intelligence); mining; neurocontrollers; robust control; self-adjusting systems; spatial variables control; three-term control; automatic control; automatic ranging cutting height; coal-mining; drum-type shearer; dynamic property; neural network based online self-learning adaptive PID control; strong robustness; strongly robust control; Adaptive control; Automatic control; Engine cylinders; Mathematical model; Mathematics; Neural networks; Programmable control; Servomechanisms; Three-term control; Valves;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1174504
