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Cooperative and non-cooperative game control strategies of the ship in collision situation

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Języki publikacji
EN
Abstrakty
EN
The paper introduces the positional cooperative and non-cooperative game of a greater number of met ships for the description of the process considered as well as for the synthesis of optimal control strategies of the own ship in collision situation. The approximated mathematical model of differential game in the form of triple linear programming problem is used for the synthesis of safe ship trajectory as a multistage process decision. The considerations have been illustrated an example of program computer simulation to determine the safe ship trajectories in situation of passing a many of the ships encountered.
Twórcy
autor
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • 1 Basar, T. & Olsder, G.J. 1982. Dynamic non-cooperative game theory. New York: Academic Press.
  • 2 Bist, D.S. 2000. Safety and security at sea. Oxford-New Delhi: Butter Heinemann.
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  • 5 Cockcroft, A.N. & Lameijer, J.N.F. 2006. The collision avoidance rules. Amsterdam-Tokyo: Elsevier.
  • 6 Engwerda, J.C. 2005. LQ dynamic optimization and differential games. West Sussex: John Wiley & Sons.
  • 7 Fletcher, R. 1987. Practical methods of optimization. New York: John Wiley and Sons.
  • 8 Gierusz, W. & Lebkowski A. 2012. The researching ship “Gdynia”. Polish Maritime Research 1(19): 11-18.
  • 9 Gluver, H. & Olsen, D. 1998. Ship collision analysis. Rotterdam-Brookfield: A.A. Balkema.
  • 10 Isaacs, R. 1965. Differential games. New York: John Wiley & Sons.
  • 11 Isil Bozma, H. & Koditschek, D.E. 2001. Assembly as a non-cooperative game of its pieces: Analysis of ID sphere assemblies. Robotica 19: 93-108.
  • 12 Jaworski B., Kuczkowski L., Smierzchalski R. 2012. Extinction event concepts fot the evolutionary algorithms. Przeglad Elektrotechniczny 10B(88): 252-255.
  • 13 Kula, K. 2015. Model-based controller for ship track-keeping using Neural Network. Proc. of IEEE 2nd Int. Conf. on Cybernetics, CYBCONF, Gdynia, Poland: 178-183.
  • 14 Lazarowska, A. 2012. Decision support system for collision avoidance at sea. Polish Maritime Research 1(19):19-24.
  • 15 Lebkowski A. 2015. Evolutionary Methods in the Management of Vessel Traffic. Edited by Weintrit A. & Neumann T.: Information, Communication and Environment Marine Navigation and Safety at Sea Transportation: 259-266.
  • 16 Lisowski J. 2011. The optimal and safe ship trajectories for different forms of neural state constraints. Edited by Kitowski Z., Garus J., Szymak P.: Mechatronic Systems, Mechanics and Materials, Book Series: Solid State Phenomena, Vol. 180: 64-69.
  • 17 Lisowski J. 2012. Game control methods in avoidance of ships collisions. Polish Maritime Research 1(19):3-10.
  • 18 Lisowski J. & Lazarowska A. 2013. The radar data transmission to computer support system of ship safety. Edited by Garus J., Szymak p., Zak B.: Mechatronic Systems, Mechanics and Materials, Book Series: Solid State Phenomena, Vol. 196: 95-101.
  • 19 Lisowski J. 2014a. Computational intelligence methods of a safe ship control. Edited by Jedrzejowicz P., Czarnowski I., Howlett R.J.: Knowledge-based and Intelligent information & Engineering systems 18th Annual Conference KES 2014, Book Series: Procedia Computer Science, Vol. 35: 634-643.
  • 20 Lisowski J. 2014b. Optimization-supported decision-making in the marine game environment. Edited by Garus J., Szymak p.: Mechatronic Systems, Mechanics and Materials, Book Series: Solid State Phenomena, Vol. 210: 215-222.
  • 21 Lisowski J. 2015a. Determination and display of safe ship trajectories in collision situations at sea. Solid State Phenomena, Vol. 236: 128-133.
  • 22 Lisowski J. 2015b. Sensitivity of the game control of ship in collision situations. Polish Maritime Research, 4(22): 27-33.
  • 23 Lisowski J. 2015c. Multi-stage safe ship control with neural state constraints. 2nd IEEE International Conference on Cybernetics, CYBCONF, Gdynia, Poland: 56-61.
  • 24 Lisowski J. 2015d. Comparison of anti-collision game trajectories of ship in good and restricted visibility at sea. Edited by Weintrit A.: Activities in Navigation - Marine Navigation and Safety of Sea Transportation, CRC Press Taylor & Francis Group, London: 201-210.
  • 25 Lisowski J. 2016a. The sensitivity of state differential game vessel traffic model. Polish Maritime Research 2(23):14-18.
  • 26 Lisowski J.: Analysis of Methods of Determining the Safe Ship Trajectory. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 10, No. 2, doi:10.12716/1001.10.02.05, pp. 223-228, 2016
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  • 28 Mesterton-Gibbons, M. 2001. An introduction to game theoretic modeling. Providence: American Mathematical Society.
  • 29 Millington, I. & Funge, J. 2009. Artificial intelligence for games. Amsterdam-Tokyo: Elsevier.
  • 30 Modarres, M. 2006. Risk analysis in engineering. Boca Raton: Taylor & Francis Group.
  • 31 Mohamed-Seghir, M. 2016. Computational intelligence method for ship trajectory planning. Proc. XXI Conference Methods and Models in Automation and Robotics, MMAR, Miedzyzdroje: 636-640.
  • 32 Nisan, N., Roughgarden, T., Tardos, E. & Vazirani, V.V. 2007. Algorithmic game theory. New York: Cambridge University Press.
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  • 35 Szlapczynski R. & Szlapczynska J. 2012. Customized crossover in evolutionary sets of safe ship trajectories. Int. Journal of Applied Mathematics and Computer Science 4(22): 999-1009.
  • 36 Tomera, M. 2015. A multivariable low speed controller for a ship autopilot with experimental results. Proc. XXth Int. Conf. Methods and Models in Automation and Robotics, MMAR, Miedzyzdroje: 17-22.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6f4d726f-5a87-446e-a615-9b40ecb9123c
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