Depth control for storage tank in-service inspection robot based on artificial intelligence control

被引:5
作者
Huang, Zhiqiang [1 ]
He, Lei [2 ]
Li, Xinxia [1 ]
Kang, Yewei [3 ]
Xie, Dou [1 ]
机构
[1] Southwest Petr Univ, Sch Mechatron Engn, Chengdu, Sichuan, Peoples R China
[2] Southwest Petr Univ, Chengdu, Sichuan, Peoples R China
[3] Natl Engn Lab Oil & Gas Pipeline Transportat Safe, PetroChina Pipeline R&D Ctr, Langfang, Peoples R China
来源
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION | 2018年 / 45卷 / 06期
关键词
Robot; ROV; Intelligence control; Storage tank in-service inspection;
D O I
10.1108/IR-05-2018-0085
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Purpose The purpose of this paper is to propose a buoyancy-gravity adjustment device and a fuzzy intelligent controller for the depth control of a storage tank in-service inspection robot. Design/methodology/approach The structure of the robot is first designed based on the construction of the bottom of a crude oil tank and explosion-proof requirements. The buoyancy-gravity adjustment system is used to control the vertical movement of the robot. The motion analysis of the robot indicates that the diving or rising process is influenced by hydrodynamic force and umbilical cord tension. Considering the nonlinear model in-depth control, a fuzzy intelligent controller is proposed to address the depth control problem. The primary fuzzy controller is used to compensate for initial error with fast response. The secondary fuzzy controller is activated by an intelligent switch to eliminate the steady error. Findings The proposed fuzzy controller can better solve the complicated hydrodynamic problem of the coupling of umbilical cord and the robot during depth control by classifying the error values of depth, velocity and acceleration. Originality/value The buoyancy-gravity adjustment device and the depth control system of the robot can move through the heating coils by safe and accurate diving or rising.
引用
收藏
页码:732 / 743
页数:12
相关论文
共 18 条
[1]   Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller [J].
Bessa, Wallace M. ;
Dutra, Max S. ;
Kreuzer, Edwin .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2008, 56 (08) :670-677
[2]   Adaptive Sliding Mode Control for Depth Trajectory Tracking of Remotely Operated Vehicle with Thruster Nonlinearity [J].
Chu, Zhenzhong ;
Zhu, Daqi ;
Yang, Simon X. ;
Jan, Gene Eu .
JOURNAL OF NAVIGATION, 2017, 70 (01) :149-164
[3]  
Cruz A.C.D., 2013, IND ROBOT, V32, P157
[4]  
Harsamizadeh Tehrani Nima, 2010, 2010 8th IEEE International Conference on Control and Automation (ICCA 2010), P814, DOI 10.1109/ICCA.2010.5524051
[5]  
Hartsell D. R., 1999, IEEE ROBOT AUTOM MAG, V6, P54
[6]  
Hu Y.D., 2006, PRINCIPLE DESIGN SER
[7]   An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle [J].
Javadi-Moghaddam, J. ;
Bagheri, A. .
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (01) :647-660
[8]   Fuzzy-logic based navigation of underwater vehicles [J].
Kanakakis, V ;
Valavanis, KP ;
Tsourveloudis, NC .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2004, 40 (01) :45-88
[9]  
Li Hongxing, 1997, Control Theory & Applications, V14, P868
[10]   Adaptive fuzzy controllers based on variable universe [J].
Li, HX .
SCIENCE IN CHINA SERIES E-TECHNOLOGICAL SCIENCES, 1999, 42 (01) :10-20