Autonomous Underwater Vehicle Trajectory Tracking Based on Physics Informed Neural Network Modeling-Reference-Cited by-同舟云学术

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Autonomous Underwater Vehicle Trajectory Tracking Based on Physics Informed Neural Network Modeling

Published:2024-05-23 Issue: Volume:92 Page:7-12
ISSN:
Container-title:2024 3rd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)
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Author:

Liu Tao¹,Huang Junhao²,Zhao Jintao²

Affiliation:

1. School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education,Zhuhai,China

2. School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University & Southern Marine Science and Engineering, Guangdong Laboratory (Zhuhai),Zhuhai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10554067/10554293/10554325.pdf?arnumber=10554325

Reference29 articles.

1. Event-triggered formation control of AUVs with fixed-time RBF disturbance observer

2. Future Trends in Marine Robotics [TC Spotlight]

3. A Unifying Task Priority Approach for Autonomous Underwater Vehicles Integrating Homing and Docking Maneuvers

4. Numerical Investigation on Hydrodynamic Performance of a Portable AUV

5. Trajectory tracking control for autonomous underwater vehicles based on dual closed-loop of MPC with uncertain dynamics

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