References
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.