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
相关论文
共 50 条
  • [21] A Melt Temperature PID Controller Based on RBF Neural Network
    Jiang, Jing
    Wen, Shengping
    Zhao, Guoping
    [J]. 2008 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL 2, PROCEEDINGS, 2008, : 172 - 175
  • [22] Adaptive Control of Wind Turbine Generator System Based on RBF-PID Neural Network
    Wang, Zhanshan
    Shen, Zhengwei
    Cai, Chao
    Jia, Kaili
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 538 - 543
  • [23] PID Adaptive Control in the Application of the Induction Motor System Based on the RBF Neural Network Inverse
    Li, Zhang
    Bo, Yu
    [J]. MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 2393 - 2396
  • [24] RBF Neural Network Based Adaptive Constrained PID Control of a Solid Oxide Fuel Cell
    Xu, Dezhi
    Yan, Wenxu
    Ji, Nan
    [J]. PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 3986 - 3991
  • [25] Analysis of speed servo system of pneumatic Manipulator based RBF neural network PID control
    Luo, Jing
    Yuan, Ruibo
    Yuan, Yubi
    Ba, Shaonan
    Zhang, Zongcheng
    [J]. ADVANCES IN SCIENCE AND ENGINEERING, PTS 1 AND 2, 2011, 40-41 : 65 - 70
  • [26] PID Control Based On Double Fuzzy RBF Neural Network For 7-DOF Manipulator
    Zhang, Hongming
    Assawinchaichote, Wudhichai
    [J]. 2020 8TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), 2020,
  • [27] Model reference adaptive PID control of hydraulic parallel robot based on RBF neural network
    Pei, Zhongcai
    Zhang, Yanfang
    Tang, Zhiyong
    [J]. 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-5, 2007, : 1383 - 1387
  • [28] Double variable PID decoupling control of turbofan engine based on RBF neural network identification
    Yang, Hua
    Guo, Ying-Qing
    [J]. Hangkong Dongli Xuebao/Journal of Aerospace Power, 2007, 22 (08): : 1391 - 1395
  • [29] Bit pressure control during drilling operation using engineering process control
    Imanian, Mahdi
    Ghassemi, Aazam
    Karbasian, Mahdi
    [J]. ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2018, 40 (18) : 2193 - 2202
  • [30] Study of PID Temperature Control for Reactor Based on RBF Network
    Zhou Yilin
    Ding Qichen
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS (ICAL), 2012, : 456 - 460
12345