RBF Neural Network Based-PID Control for Weight on Bit During Drilling Operation

被引：0
|
作者：
Ma, Sike [1 ,2 ]
Wu, Min [1 ,2 ]
Chen, Luefeng [1 ,2 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Hubei, Peoples R China
来源：
2018 37TH CHINESE CONTROL CONFERENCE (CCC) | 2018年
基金：
中国国家自然科学基金;
关键词：
Weight on bit; auto-drilling system; RBF neural network; PID; STICK-SLIP; BOUNCE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Deep drilling is a costly project and efficiency is of paramount importance. The weight on bit is one of the main operating parameters that influences the drilling efficiency and it was controlled by manual before. But after people saw the giant potential of an auto-drilling system in increasing the drilling efficiency, more and more studies on the feed back control of weight on hit have emerged. This paper mainly studied weight on bit dynamic under the variational formation based on a lumped parameter model and a self-tuning PID controller for weight on bit control. The parameters of the PID controller are tuned by using gradient descent method and RBF neural network identification.
引用
收藏
页码：10311 / 10314
页数：4
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