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Event-triggered adaptive neural network trajectory tracking control for underactuated ships under uncertain disturbance

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.
Rocznik
Tom
Strony
119--131
Opis fizyczny
Bibliogr. 40 poz., rys.
Twórcy
autor
  • School of Navigation and shipping, Shandong Jiaotong University, China
autor
  • School of Navigation and shipping, Shandong Jiaotong University, China
autor
  • University of Toronto OISE, China
Bibliografia
  • 1. 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.
  • 2. 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.
  • 3. 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.
  • 4. K. Jonghoek, ‘Target following and close monitoring using an unmanned surface vehicle,’ IEEE Transactions on Systems Man & Cybernetics Systems, 2020, 50(11): 42334242, doi: 10.1109/TSMC.2018.2846602.
  • 5. 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.
  • 6. 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.
  • 7. 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.
  • 8. 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.
  • 9. 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.
  • 10. 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.
  • 11. 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.
  • 12. 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.
  • 13. N. Wang, Z. Sun, and J. Yin, ‘Fuzzy unknown observerbased 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.
  • 14. 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.
  • 15. 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.
  • 16. 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.
  • 17. S. Wang and Y. Tuo, ‘Robust trajectory tracking control of underactuated surface vehicles with prescribed performance,’ Polish Maritime Research, 2020, 27(4): 148156, doi: 10.2478/pomr-2020-0075.
  • 18. N. Wang, Y. Gao, and H. Zhao, ‘Reinforcement learningbased 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.
  • 19. 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.
  • 20. 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.
  • 21. 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.
  • 22. N. Wang and C. K. Ahn, ‘Hyperbolic-tangent LOS guidancebased finite-time path following of underactuated marine vehicles,’ IEEE Transactions on Industrial Electronics, 2020, 67(10): 8566-8575, doi: 10.1109/TIE.2019.2947845.
  • 23. 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.
  • 24. 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.
  • 25. 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.
  • 26. A. Girard, ‘Dynamic triggering mechanisms for eventtriggered control,’ IEEE Transactions on Automatic Control, 2013, 60(7): 1992-1997, doi: 10.1109/TAC.2014.2366855.
  • 27. W. Heemels and M. Donkers, ‘Model-based periodic eventtriggered control for linear systems,’ Automatica, 2013, 49 (3): 698-711, doi: 10.1016/j.automatica.2012.11.025.
  • 28. 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.
  • 29. 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.
  • 30. 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.
  • 31. 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.
  • 32. 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.
  • 33. Q. Zhang, G. Zhu, and X. Hu, ‘Adaptive neural network autoberthing control of marine ships,’ Ocean Engineering, 2019, 177(APR.1): 40-48, doi: 10.1016/j.oceaneng.2019.02.031.
  • 34. 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.
  • 35. 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.
  • 36. 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.
  • 37. 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.
  • 38. 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.
  • 39. 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.
  • 40. 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.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e1567fdc-5311-40f3-8523-b3a520ab2235
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