This work is licensed under the Creative Commons Attribution 4.0 International License.
H. W. He, Z. J. Zou, and Z. H. Zeng, ‘Adaptive neural network-sliding mode path following control for underactuated surface vessels,’ Journal of Shanghai Jiaotong University, 2020, 54(09): 890-897, doi: 10.16183/j. cnki.jsjtu.2019.122.Search in Google Scholar
H. Y. Xu, M. F. Zhu, W. Z. Yu, and X. Han, ‘Robust adaptive control of automatic berthing for intelligent ships,’ Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020, 48(03): 25-29+40, doi: 10.13245/j.hust.200305.Search in Google Scholar
N. Wang and H. R. Karimi, ‘Successive waypoints tracking of an underactuated surface vehicle,’ IEEE Transactions on Industrial Informatics, 2020, 16(2): 898-908, doi: 10.1109/TII.2019.2922823.Search in Google Scholar
K. Jonghoek, ‘Target following and close monitoring using an unmanned surface vehicle,’ IEEE Transactions on Systems Man & Cybernetics Systems, 2020, 50(11): 4233-4242, doi: 10.1109/TSMC.2018.2846602.Search in Google Scholar
G. Zhu, Y. Ma, and S. Hu, ‘Single parameter learning based finite-time tracking control of underactuated MSVs under input saturation,’ Control Engineering Practice, 2020, 105, doi: 10.1016/j.conengprac.2020.104652.Search in Google Scholar
G. Zhu, Y. Ma, and Z. Li, ‘Event-triggered adaptive neural fault-tolerant control of underactuated MSVs with input saturation,’ IEEE Transactions on Intelligent Transportation Systems, 2021, PP (99): 1-13, doi: 10.1109/TITS.2021.3066461.Search in Google Scholar
Y. Ma, G. Zhu, and Z. L, ‘Error-driven-based nonlinear feedback recursive design for adaptive NN trajectory tracking control of surface ships with input saturation,’ IEEE Intelligent Transportation Systems Magazine, 2019, PP (2): 1-1. doi:10.1109/MITS.2019.2903517.Search in Google Scholar
C. J. Zhang, C. Wang, and W. Cao, ‘Underactuated USV neural network adaptive trajectory tracking control,’ Journal of Harbin Institute of Technology, 2020, 52(12): 7-13 doi: 10.11918/201905049.Search in Google Scholar
W. J. Wang and J. Li, ‘A direct adaptive sliding mode trajectory tracking control design based on an RBF neural network,’ Machinery Design & Manufacture, 2020(11): 183-187, doi: 10.19356/j.cnki.1001-3997.2020.11.046.Search in Google Scholar
N. Wang and H. He, ‘Dynamics-level finite-time fuzzy monocular visual servo of an unmanned surface vehicle,’ IEEE Transactions on Industrial Electronics, 2020, 67(11): 9648-9658, doi: 10.1109/TIE.2019.2952786.Search in Google Scholar
Y. Cheng, Z. Sun, and Y Huang, ‘Fuzzy categorical deep reinforcement learning of a defensive game for an unmanned surface vessel,’ International Journal of Fuzzy Systems, 2019, 21(2): 592-606, doi: 10.1007/s40815-018-0586-0.Search in Google Scholar
Y. Lu, ‘Adaptive-fuzzy control compensation design for direct adaptive fuzzy control,’ IEEE Transactions on Fuzzy Systems, 2018, 26(6): 3222-3231, doi: 10.1109/TFUZZ.2018.2815552.Search in Google Scholar
N. Wang, Z. Sun, and J. Yin, ‘Fuzzy unknown observer-based robust adaptive path following control of underactuated surface vehicles subject to multiple unknowns,’ Ocean Engineering, 2019, 176: 57-64, doi: 10.1016/j.oceaneng.2019.02.017.Search in Google Scholar
Y. Deng, X. Zhang, and N. Im, ‘Adaptive fuzzy tracking control for underactuated surface vessels with unmodeled dynamics and input saturation,’ ISA Transactions, 2020, 103, doi: 10.1016/j.isatra.2020.04.010.Search in Google Scholar
D. Mu, G. Wang, and Y. Fan, ‘Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation,’ Neural Computing and Applications, 2021, (6), doi: 10.1007/s00521-021-05922-x.Search in Google Scholar
X. Zhang, ‘Backstep sliding mode control for trajectory tracking of underactuated surface unmanned vehicles,’ Digital Technology & Application, 2020, 38(01): 180-183, doi: CNKI:SUN:SZJT.0.2020-01-090.Search in Google Scholar
S. Wang and Y. Tuo, ‘Robust trajectory tracking control of underactuated surface vehicles with prescribed performance,’ Polish Maritime Research, 2020, 27(4): 148-156, doi: 10.2478/pomr-2020-0075.Search in Google Scholar
N. Wang, Y. Gao, and H. Zhao, ‘Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle,’ IEEE Transactions on Neural Networks and Learning Systems, 2020, PP(99): 1-12, doi: 10.1109/TNNLS.2020.3009214.Search in Google Scholar
B. Qiu, G. Wang, and Y. Fan, ‘Path following of underactuated unmanned surface vehicle based on trajectory linearization control with input saturation and external disturbances,’ International Journal of Control Automation and Systems, 2020, 18(4): 1-12, doi: 10.1007/s12555-019-0659-3.Search in Google Scholar
Q. Zhang, Z. Ding, and M. Zhang, ‘Adaptive self-regulation PID control of course-keeping for ships,’ Polish Maritime Research, 2020, 27(1): 39-45, doi: 10.2478/pomr-2020-0004.Search in Google Scholar
D. D. Wang, Q. Zong, and B. Y. Zhang, ‘Fully distributed limited-time formation control of multiple UAVs,’ Control and Decision, 2019, 34(12): 154-158, doi: 10.13195/j. kzyjc.2018.0314.Search in Google Scholar
N. Wang and C. K. Ahn, ‘Hyperbolic-tangent LOS guidance-based finite-time path following of underactuated marine vehicles,’ IEEE Transactions on Industrial Electronics, 2020, 67(10): 8566-8575, doi: 10.1109/TIE.2019.2947845.Search in Google Scholar
M. Y. Hu, S. H. Yu, and Y. Y. Li. ‘Finite time trajectory tracking control of ocean surface vessels based on command filtering with full state constraints,’ Journal of Nanjing University of Science and Technology, 2021, 45(3): 10, doi: 10.14177/j.cnki.32-1397n.2021.45.03.003.Search in Google Scholar
H. L. Chen, H. X. Ren, and B. C. Yang, ‘Design of finite time controller for ship dynamic positioning based on LS-SVM,’ Ship Engineering, 2020, 42(2): 8, doi: 10.13788/j. cnki.cbgc.2020.02.14.Search in Google Scholar
P. Tabuada, ‘Event-triggered real-time scheduling of stabilizing control tasks,’ IEEE Transactions on Automatic Control, 2007, 52(9): 1680-1685, doi: 10.1109/TAC.2007.904277.Search in Google Scholar
A. Girard, ‘Dynamic triggering mechanisms for event-triggered control,’ IEEE Transactions on Automatic Control, 2013, 60(7): 1992-1997, doi: 10.1109/TAC.2014.2366855.Search in Google Scholar
W. Heemels and M. Donkers, ‘Model-based periodic event-triggered control for linear systems,’ Automatica, 2013, 49 (3): 698-711, doi: 10.1016/j.automatica.2012.11.025.Search in Google Scholar
S. Gao, Z. Peng, and L. Liu, ‘Coordinated target tracking by multiple unmanned surface vehicles with communication delays based on a distributed event-triggered extended state observer,’ Ocean Engineering, 2021, 227(4): 108283, doi: 10.1016/j.oceaneng.2020.108283.Search in Google Scholar
S. J. Yoo and B. S. Park, ‘Guaranteed connectivity based distributed robust event-triggered tracking of multiple underactuated surface vessels with uncertain nonlinear dynamics,’ Nonlinear Dynamics, 2020, 99(3): 2233-2249, doi: 10.1007/s11071-019-05432-5.Search in Google Scholar
Y. Deng, X. Zhang, and N. Im, ‘Model-based event-triggered tracking control of underactuated surface vessels with minimum learning parameters,’ IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 1-14, doi: 10.1109/TNNLS.2019.2951709.Search in Google Scholar
F. Wang, B. Chen, and X. Liu, ‘Finite-time adaptive fuzzy tracking control design for nonlinear systems,’ IEEE Transactions on Fuzzy Systems, 2017, 26(3), 1207-1216, doi: 10.1109/TFUZZ.2017.2717804.Search in Google Scholar
W. T. Wu, N. Gu, and Z. H. Peng, ‘Distributed time-varying formation control of multi-pilot guided unmanned ship swarms,’ Chinese Journal of Ship Research, 2020, 15(01): 21-30, doi: 10.19693/j.issn.1673-3185.01734.Search in Google Scholar
Q. Zhang, G. Zhu, and X. Hu, ‘Adaptive neural network auto-berthing control of marine ships,’ Ocean Engineering, 2019, 177(APR.1): 40-48, doi: 10.1016/j.oceaneng.2019.02.031.Search in Google Scholar
B. Xu and Y. Shou, ‘Composite learning control of MIMO systems with applications,’ IEEE Transactions on Industrial Electronics, 2018, PP(99):1-1, doi: 10.1109/TIE.2018.2793207.Search in Google Scholar
M. Li, T. Li, and X. Gao, ‘Adaptive NN event-triggered control for path following of underactuated vessels with finite-time convergence,’ Neurocomputing, 2020, 379(Feb.28): 203-213, doi: 10.1016/j.neucom.2019.10.044.Search in Google Scholar
Q. Zhang, M. Zhang, and R. Yang, ‘Adaptive neural finite-time trajectory tracking control of MSVs subject to uncertainties,’ International Journal of Control Automation and Systems, 2021, 19(6): 2238-2250, doi: 10.1007/s12555-020-0130-5.Search in Google Scholar
Y. Huang and Y. Jia, ‘Adaptive fixed-time six-DOF tracking control for noncooperative spacecraft fly-around mission,’ IEEE Transactions on Control Systems Technology, 2019, 27(4): 1-9, doi: 10.1109/TCST.2018.2812758.Search in Google Scholar
R. Skjetne, T. I. Fossen, and P. V. Kokotovi, ‘Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory,’ Pergamon Press, Inc. 2005, 41(2): 289-298.Search in Google Scholar
C. Y. Wu, L. L. Fan, and H. H. Ji, ‘Finite-time consensus control by using adaptive neural networks control,’ Engineering of China, 2022, 29(03): 455-463, doi: 10.14107/j. cnki.kzgc.20210489.Search in Google Scholar
Q. Zhang, Y. C. Hu, and A. Q. Wang, ‘Nonlinear adaptive control algorithm based on dynamic surface control and neural networks for ship course-keeping controller,’ Journal of Applied Science and Engineering, 2017, 20(2): 157-163, doi: 10.6180/jase.2017.20.2.03.Search in Google Scholar
, 