Adaptive Control for Autonomous Underwater Vehicles without Kinetic Model using Deep Neural Network-Reference-Cited by-同舟云学术

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Adaptive Control for Autonomous Underwater Vehicles without Kinetic Model using Deep Neural Network

Published:2024-05-17 Issue: Volume: Page:1923-1928
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Jiang Shihui¹,Zhang Tianbo²,Fang Jing¹,Liu Xing¹,Shen Dong³

Affiliation:

1. CSSC Systems Engineering Research Institute,Beijing,P. R. China

2. Tsinghua University,Beijing National Research Center for Info. Sci. & Tech. (BNRist),Department of Automation,Beijing,P. R. China,100084

3. School of Mathematics, Renmin University of China,Beijing,P. R. China,100872

Funder

National Natural Science Foundation (NNSF) of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606810.pdf?arnumber=10606810

Reference24 articles.

1. QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles

2. Model Predictive Adaptive Constraint Tracking Control for Underwater Vehicles

3. A Bioinspired Filtered Backstepping Tracking Control of 7000-m Manned Submarine Vehicle

4. A recurrent neural network based fuzzy sliding mode control for 4-DOF ROV movements

5. Backstepping and Sliding Mode Control for AUVs Aided with Bioinspired Neurodynamics

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