Composite learning adaptive sliding mode control for AUV target tracking

被引：42

作者：

Guo, Yuyan ^{[1
,2
]}

Qin, Hongde ^{[3
]}

Xu, Bin ^{[1
,3
]}

Han, Yi ^{[1
]}

Fan, Quan-Yong ^{[1
]}

Zhang, Pengchao ^{[4
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710000, Shaanxi, Peoples R China

[2] State Key Lab Robot & Syst HIT, Harbin 150000, Heilongjiang, Peoples R China

[3] Harbin Engn Univ, Sci & Technol Underwater Vehicle Lab, Harbin 150000, Heilongjiang, Peoples R China

[4] Shaanxi Univ Technol, Key Lab Ind Automat Shaanxi Prov, Hanzhong 723000, Shaanxi, Peoples R China

来源：

NEUROCOMPUTING | 2019年 / 351卷

基金：

中国国家自然科学基金;

关键词：

Autonomous underwater vehicle; Target tracking; Sliding mode control; Composite learning; Neural networks; AUTONOMOUS UNDERWATER VEHICLE; SYSTEMS; INPUT; NETWORKS; DESIGN;

D O I：

10.1016/j.neucom.2019.03.033

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies the controller design for an autonomous underwater vehicle (AUV) with the target tracking task. Considering the uncertainty the nonlinear longitudinal model, a sliding mode controller is designed. Meanwhile the neural networks (NNs) are used to approximate the unknown nonlinear function in the model. To improve the NNs learning rapidity, the prediction error which reflect the learning performance is constructed, further the updating law is designed utilizing the composite learning technique. The system stability is guaranteed through the Lyapunov approach. The simulation results verify that the designed method could force the AUV to track the target until rendezvous, and the model uncertainty is addressed better via the composite learning algorithm. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：180 / 186

页数：7

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