期刊
NEUROCOMPUTING
卷 111, 期 -, 页码 184-189出版社
ELSEVIER
DOI: 10.1016/j.neucom.2012.12.026
关键词
Autonomous underwater vehicle; Neural network; Trajectory tracking; Minimal learning parameter
资金
- National Natural Science Foundation of China [51179019, 51079031]
- Natural Science Foundation of Liaoning Province [20102012]
- Program for Liaoning Excellent Talents in University (LNET) [LR2012016]
- Natural Science Foundation of Fujian Province [2010J01004]
In this paper, a novel adaptive neural network (NN) controller is proposed for trajectory tracking of autonomous underwater vehicle (AUV). By employing radial basic function neural network to account for modeling errors, then the adaptive NN tracking controller is constructed by combining the dynamic surface control (DSC) and the minimal learning parameter (MLP). The proposed controller guarantees that all the close-loop signals are uniform ultimate bounded (UUB) and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the effectiveness of the proposed algorithm. (C) 2013 Elsevier B.V. All rights reserved.
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