Reinforcement learning-based distributed cooperative sliding mode control for unmanned surface vehicles
Author:
Funder
National Natural Science Foundation of China
Excellent Youth Program of Ningxia Natural Science Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-10253-8.pdf
Reference21 articles.
1. Yang X, Yan X, Liu W, Ye H, Du Z, Zhong W (2022) An improved Stanley guidance law for large curvature path following of unmanned surface vehicle. Ocean Eng 266:112797
2. Sun H, Shi J, Hou L (2023) Event-triggered observer-based T-S fuzzy dynamic positioning fault-tolerant control for unmanned surface vehicle. Neural Comput Appl 35:1–15
3. Mu D, Wang G, Fan Y (2021) Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation. Neural Comput Appl 33:12777–12789
4. Wu J, Li R, Li J, Zou M, Huang Z (2023) Cooperative unmanned surface vehicles and unmanned aerial vehicles platform as a tool for coastal monitoring activities. Ocean Coast Manag 232:106421
5. Xia J, Luo Y, Liu Z, Zhang Y, Shi H, Liu Z (2022) Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning. Def Technol 29:80
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