期刊
NEUROCOMPUTING
卷 351, 期 -, 页码 180-186出版社
ELSEVIER
DOI: 10.1016/j.neucom.2019.03.033
关键词
Autonomous underwater vehicle; Target tracking; Sliding mode control; Composite learning; Neural networks
资金
- National Natural Science Foundation of China [61622308, 61873206]
- National Ten Thousand Talent Program for Young Top-notch Talents [W03070131]
- Fok Ying-Tong Education Foundation [161058]
- Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology [SXJQR2018WDKT05]
- State Key Laboratory of Robotics and System (HIT) [SKLRS-2018-KF-13]
- Key R&D Program of Shaanxi Province [2017GY-044]
- Qinling-Bashan Mountains Bioresources Comprehensive Development C.I.C. [QBXT-17-7]
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.
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