A DSC and MLP based robust adaptive NN tracking control for underwater vehicle

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• 作者：Baobin Miao^aAuthor Vitae ; Tieshan Li^a ; ^{tieshanli@126.com} ; ^{tieshanlidmu@gmail.com}Author Vitae ; Weilin Luo^bAuthor Vitae
• 关键词：Autonomous underwater vehicle ; Neural network ; Trajectory tracking ; Minimal learning parameter
• 刊名：Neurocomputing
• 出版年：2013
• 出版时间：2 July, 2013
• 年：2013
• 卷：111
• 期：Complete
• 页码：184-189
• 全文大小：529 K

文摘

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.