Design of Neural Network Control System for Controlling Trajectory of Autonomous Underwater Vehicles

被引：23
作者：
Eski, Ikbal [1 ]
Yildirim, Sahin [2 ]
机构：
[1] Erciyes Univ, Dept Mechatron Engn, Kayseri, Turkey
[2] Erciyes Univ, Fac Engn, Mechatron Engn Dept, Kayseri, Turkey
来源：
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS | 2014年 / 11卷
关键词：
Neural Network Control; Robust Control; Autonomous Underwater Vehicles; Trajectory Control;
D O I：
10.5772/56740
中图分类号：
TP24 [机器人技术];
学科分类号：
080202 ; 1405 ;
摘要：
A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.
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页数：17
相关论文
共 17 条
[1]   Designing a Fuzzy-like PD controller for an underwater robot [J].
Akkizidis, IS ;
Roberts, GN ;
Ridao, P ;
Batlle, J .
CONTROL ENGINEERING PRACTICE, 2003, 11 (04) :471-480
[2]  
Aranda J., 2004, 2004 PGM SPAIN
[3]   An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles [J].
Bessa, Wallace M. ;
Dutra, Max S. ;
Kreuzer, Edwin .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2010, 58 (01) :16-26
[4]  
Fossen TI., 1994, Guidance and Control of Ocean Vehicles
[5]   Decoupled PD set-point controller for underwater vehicles [J].
Herman, Przemyslaw .
OCEAN ENGINEERING, 2009, 36 (6-7) :529-534
[6]   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
[7]   Development of a real-time control architecture for a semiautonomous underwater vehicle for intervention missions [J].
Kim, TW ;
Yuh, J .
CONTROL ENGINEERING PRACTICE, 2004, 12 (12) :1521-1530
[8]   Application of on-line neuro-fuzzy controller to AUVs [J].
Kim, TW ;
Yuh, J .
INFORMATION SCIENCES, 2002, 145 (1-2) :169-182
[9]   Robust trajectory control of underwater vehicles using time delay control law [J].
Kumar, R. Prasanth ;
Dasgupta, A. ;
Kumar, C. S. .
OCEAN ENGINEERING, 2007, 34 (5-6) :842-849
[10]   Robust diving control of an AUV [J].
Lapierre, Lionel .
OCEAN ENGINEERING, 2009, 36 (01) :92-104
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