Research on oil rig automatic feed drilling system based on MOBP neural network

doi:10.1109/ICICIP.2013.6568031

CORC > 兰州理工大学 > 兰州理工大学

	Research on oil rig automatic feed drilling system based on MOBP neural network
	Li, Zhang; Jian, Liu; Lei, Wei
	2013
会议日期	June 9, 2013 - June 11, 2013
会议地点	Beijing, China
关键词	Controllers Fuzzy control Infill drilling Intelligent control MATLAB Three term control systems Drilling parameters Improved BP neural network MOBP Modular neural networks Optimization algorithms Precise control Steady state errors System performance
DOI	10.1109/ICICIP.2013.6568031
页码	15-18
英文摘要	This paper focus on AC frequency conversion electric drill of automatic feed drilling system and an MOBP neural network is used to control the WOB (weight on bit) and realize the research of constant pressure automatic feed drilling. The same type of high quality wells' drilling parameter is normalized as a network training set. A more effective optimization algorithm called momentum method is used to design a suitable improved BP neural network for automatic feed drilling system. Modular neural network is established by Matlab/Simulink and compared with conventional PID controller and Fuzzy controller. The simulation results show that in the condition of hysteresis, MOBP has better stability, better robustness and smaller steady-state error than conventional PID and Fuzzy control. The application of Neural Network in automatic feed drilling system has a significance of guidance to improve the performance. © 2013 IEEE.
会议录	Proceedings of the 2013 International Conference on Intelligent Control and Information Processing, ICICIP 2013
会议录出版者	IEEE Computer Society
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117422]
专题	兰州理工大学
作者单位	School of Mechanical and Electronically Engineering, LAN Zhou University of Technology, Lan Zhou, 730050, Gansu, China
推荐引用方式 GB/T 7714	Li, Zhang,Jian, Liu,Lei, Wei. Research on oil rig automatic feed drilling system based on MOBP neural network[C]. 见:. Beijing, China. June 9, 2013 - June 11, 2013.