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Tytuł artykułu

Balanced Objective Model Predictive Control for Distance-Keeping and Tracking of Manoeuvring Vessels

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Maintaining a specified distance from target vessels is a common requirement in maritime management. The tracking control is inherently complex, demanding both accurate target tracking and frequent adjustments to the propeller and rudder, which can lead to increased energy consumption and accelerated mechanical wear. This study introduces a distance-keeping tracking model for manoeuvring marine vessels, along with a balanced objective model predictive control (BOMPC) algorithm. BOMPC was developed based on the Marine Manoeuvring Group (MMG) dynamics model. Beyond prioritising the tracking accuracy, the algorithm incorporates the propeller speed and rudder angle from the dynamics model as optimisation criteria within the MPC framework. This enables the simultaneous control of the tracking vessel’s speed and heading, comprehensively addressing both the tracking accuracy requirements of target tracking and the considerations of energy consumption and mechanical wear. The accuracy and effectiveness of the proposed target tracking model and control algorithm are validated through both simulation and experiments. This research has potential applications in maritime management, marine search and rescue, and related domains.
Słowa kluczowe
Rocznik
Tom
Strony
33--39
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
  • School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
  • China Ship Development and Design Center, Wuhan, China
autor
  • School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Key Laboratory of Marine Intelligent Equipment and System, Shanghai Jiao Tong University, Ministry of Education, Shanghai, China
autor
  • China Ship Development and Design Center, Wuhan, China
  • Key Laboratory of Marine Intelligent Equipment and System, Shanghai Jiao Tong University, Ministry of Education, Shanghai, China
autor
  • Key Laboratory of Marine Intelligent Equipment and System, Shanghai Jiao Tong University, Ministry of Education, Shanghai, China
Bibliografia
  • 1. Han S, Sun JH, Zhou L. A potential field-based model predictive target following controller for underactuated unmanned surface vehicles. IEEE Transactions on Vehicular Technology 2024, 73(10): 14510–14524.
  • 2. Guo R, Mao Y, Xiang ZQ. Research on LSTM-based maneuvering motion prediction for USVs. Journal of Marine Science and Engineering 2024, 12, 1661. https://doi.org/10.3390/jmse12091661.
  • 3. Zhang H, Huang Y, Qin H, Geng Z. USV search mission planning methodology for lost target rescue on sea. Electronics 2023, 12(22): 4584. https://doi.org/10.3390/electronics12224584.
  • 4. Er MJ, Ma C, Gong HB. Intelligent motion control of unmanned surface vehicles: A critical review. Ocean Engineering 2023, 280: 115113.
  • 5. Breivik M, Hovstein VE, Fossen TI. Straight-line target tracking for unmanned surface vehicles. Modeling, Identification and Control 2008, 29(4): 131–149. https://doi.org/10.4173/mic.2008.4.2.
  • 6. Li LG, Pei ZY, Jin JC, Dai YS. Control of unmanned Surface vehicle along the desired trajectory using improved line of sight and estimated sideslip angle. Polish Maritime Research 2021, 28(2): 18–26. https://doi.org/10.2478/pomr-2021-0017.
  • 7. Zhang WJ, Wang FQ, Gao QQ, Qu XR. Navigation situation assessment of autonomous surface vehicles in a cooperative hunting environment. Polish Maritime Research 2022, 29(2): 19–26. https://doi.org/10.2478/pomr-2022-0013.
  • 8. Zhang YY, Wang ZH, Zou ZJ. Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal. Ocean Engineering 2022, 257, 111279.
  • 9. Saksvik IB, Alcocer A, Hassani V. Target tracking of an underwater glider using a small unmanned surface vessel. IFAC-PapersOnLine 2022, 55(31): 478–483.
  • 10. Teng Y, Liu Z, Zhang L, et al. On intelligent ship tracking control method based on hierarchical MPC. Proceedings of the 2021 40th Chinese Control Conference (CCC). IEEE, 2021: 2814–2819.
  • 11. Kim J. Target following and close monitoring using an unmanned surface vehicle. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, 50(11): 4233–4242.
  • 12. Kim J. Optimal motion controllers for an unmanned Surface vehicle to track a maneuvering underwater target based on coarse range-bearing measurements. Ocean Engineering 2020, 216: 107973. https://doi.org/10.1016/j.oceaneng.2020.107973.
  • 13. Chen Z, Guo Y, Wang Q. Research on target tracking system of unmanned surface vehicle based on hierarchical control strategy. Proceedings of the 41st Chinese Control Conference (CCC). IEEE, 2022: 3651–3655.
  • 14. Agrawal P, Dolan JM. COLREGS-compliant target following for an unmanned surface vehicle in dynamic environments. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 1065–1070.
  • 15. Han S, Yan LX, Sun JH, Ding SF, Li F, Zhou L. Automatic unberthing for underactuated unmanned surface vehicle: Model-based planning and control approaches in constricted harbors. Ocean Engineering 2024, 312: 119059.
  • 16. Li SJ, Liu JL, Wu Q. Automatic docking for underactuated ships based on multi-objective nonlinear model predictive control. IEEE Access 2020, 8: 70044–70057.
  • 17. Chen HZ, Yan HC, Zhang D. Reinforcement learning-based close formation control for underactuated surface vehicle with prescribed performance and time-varying state constraints. Ocean Engineering 2022, 256: 111361.
  • 18. Wang SZ, Sun ZY, Hsieh TH. Autonomous piloting and berthing based on long short time memory neural networks and nonlinear model predictive control algorithm. Ocean Engineering 2022, 264(10):112269.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2e2b29e4-2a9e-4eaf-85b8-1cd9b4064fdc
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