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Dynamic positioning capability assessment based on optimal thrust allocation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents an efficient method of optimal thrust allocation over the actuators in a dynamically positioned ship, according to the DNV-ST-0111 standard, Level 1. The optimisation task is approximated to a convex problem with linear constraints and mathematically formulated as quadratic programming. The case study is being used to illustrate the use of the proposed approach in assessing the DP capability of a rescue ship. The quadratic programmingbased approach applied for dynamic positioning capability assessment allows for fast calculations to qualitatively compare different ship designs. In comparison with the DNV tool, it gives 100% successful validation for a ship with azimuth thrusters and a pessimistic solution for a ship equipped with propellers with rudders. Therefore, it can be safely applied at an early design stage.
Rocznik
Tom
Strony
28--38
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Bibliografia
  • 1. DNV, DNV-ST-0111, Assessment of station keeping capability of dynamic positioning vessels, DNV, 2021.
  • 2. M. Tomera, “Dynamic positioning system for a ship on harbour manoeuvring with different observers. Experimental Results,” Polish Maritime Research, 2014.
  • 3. M. Tomera, “Dynamic positioning system design for “Blue Lady”. Simulation tests,” Polish Maritime Research, 2012.
  • 4. T. Fossen, Handbook of Marine Craft Hydrodynamics and Motion Control, 1st ed. New York: John Wiley, 2011.
  • 5. A. Sørensen, „Marine Control Systems. Propulsion and Motion Control of Ships and Ocean Structures,” Lecture Notes, Department of Marine Technology. Norwegian University of Science and Technology, 2013.
  • 6. C. de Wit, „Optimal thrust allocation methods for dynamic positioning of ships,” M.Sc. thesis, Delft University of Technology, 2009.
  • 7. S. Luke, “Essentials of Metaheuristics,” in Lecture Notes, Second Edition , 2016.
  • 8. J. Ming and Y. Bowen, „The optimal thrust allocation based on QPSO algorithm for dynamic positioning vessels,” Tianjin, China, 2014, doi: 10.1109/ICMA.2014.6885898.
  • 9. X. Yang, “Optimization and metaheuristic algorithms in engineering,” in Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, 2013, pp. 1-23.
  • 10. G. Ding, P. Gao, X. Zhang, and Y. Wang, “Thrust allocation of dynamic positioning based on improved differential evolution algorithm,” in Proc. 39th Chinese Control Conference, Shenyang, China, doi: 10.23919/ CCC50068.2020.9188704, 2020.
  • 11. R. Storn and K. Price, “Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces,” Journal of Global Optimization, vol. 23, no. 1, 1995.
  • 12. D. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, 1989.
  • 13. T. Baetz-Beielstein, „Overview: Evolutionary Algorithms,” Ph.D. project, Cologne University of Applied Sciences, 2014.
  • 14. M. Kochenderfer and T. Wheeler, Algorithms for Optimisation. MIT Press, 2019.
  • 15. E. Baeyens, A. Herreros, and J. Perán, “A direct search algorithm for global optioptimization,” Algorithms, vol. 9, no. 2, p. 40, 2016, https://doi.org/10.3390/a9020040.
  • 16. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002, doi: 1109/4235.996017.
  • 17. D. Gao, X. Wang, T. Wang, Y. Wang, and X. Xu, “Optimal thrust allocation strategy of electric propulsion ship based on improved non-dominated sorting genetic algorithm II,” IEEE Access, vol. 7, no. 1, pp.135247-135255, 2019, doi: 10.1109/ACCESS.2019.2942170, 2019.
  • 18. F. Mauro and R. Nabergoj, “Advantages and disadvantages of thruster allocation procedures in preliminary dynamic positioning predictions,” Ocean Eng., vol. 123, pp. 96-102, 2016, https://doi.org/10.1016/j.oceaneng.2016.06.045.
  • 19. O. Harkegard, “Dynamic control allocation using constrained quadratic programming,” J. Guid. Contr. Dynam., vol. 27, no. 6, pp. 1028–1034, 2004, https://doi. org/10.2514/1.11607.
  • 20. Y. Luo, A. Serrani, S. Yurkovich, D. B. Doman, and M. W. Oppenheimer, “Model predictive dynamic control allocation with actuator dynamics,” in IEEE Proc. 2004 American Control Conference, pp. 1695–1700, 2004, doi: 10.23919/ACC.2004.1386823.
  • 21. A. Witkowska and R. Śmierzchalski, “Adaptive backstepping tracking control for an over-actuated DP marine vessel with inertia uncertainties,” Int. J. Appl. Math. Comput. Sci., vol. 28, no. 4, pp. 679–693, 2018, doi: 10.2478/ amcs-2018-0052.
  • 22. J. Tjønnås and T. Johansen, “Adaptive control allocation,” Automatica, vol. 44, pp. 2754-2766, 2008, https://doi. org/10.1016/j.automatica.2008.03.031.
  • 23. M. Valčič, „Optimization of thruster allocation for dynamically positioned marine vessels,” Doctoral thesis, University of Rijeka, 2020.
  • 24. E. Ruth, „Propulsion control and thrust allocation on marine vessels,” Doctoral thesis, Norwegian University of Science and Technology, 2008.
  • 25. L. Wang, J. Yang, and S. Xu, “Dynamic positioning capability analysis for marine vessels based on a DPCap polar plot program,” China Ocean Eng., vol. 32, no. 1, pp. 90-98, 2018, doi: 10.1007/s13344-018-0010-4.
  • 26. P. Zalewski, “Constraints in allocation of thrusters in a DP simulator,” Sci. J. Mar.Univ. Szczecin, vol. 52, no. 124, pp. 45-50, 2017, doi: 10.17402/244.
  • 27. P. Zalewski, “Convex optimization of thrust allocation in a dynamic positioning system,” Sci. J. Mar.Univ. Szczecin, vol. 48, no. 120, pp. 58-62, 2016, doi: 10.17402/176.
  • 28. D. Goldfarb and A. Idnani, “A numerically stable dual method for solving strictly convex quadratic programs,” Mathematical Programming, vol. 27, pp. 1-33, 1983.
Uwagi
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-fc68f9d5-070f-4edf-95b4-3d94a27edaf9
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