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2019 | 26 | 1 |

Tytuł artykułu

Analysis of impact of ship model parameters on changes of control quality index in ship dynamic positioning system

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
In this work there is presented an analysis of impact of ship model parameters on changes of control quality index in a ship dynamic positioning system designed with the use of a backstepping adaptive controller. Assessment of the impact of ship model parameters was performed on the basis of Pareto-Lorentz curves and ABC method in order to determine sets of the parameters which have either crucial, moderate or low impact on objective function. Simulation investigations were carried out with taking into account integral control quality indices

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

1

Opis fizyczny

p.6-14,fig.,ref.

Twórcy

  • Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
autor
  • Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland

Bibliografia

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  • 4. Cover, T.M.: Geometrical and Statistical Properties of System s of Linear Inequalities with Application s in Pat ter n Recognition, IEEE Transactions on Electronic Computers 1965, pp. 326-334.
  • 5. Cpałka, K.: Design of Interpretable Fuzzy Systems, Springer 2017.
  • 6. Du, J., X. Hu, H. Liu, C.L.P. Chen: Adaptive robust output feedback control for a marine dynamic positioning system based on a high-gain observer, IEEE Transactions on Neural Networks and Learning Systems. 26, 2015, pp. 2775–2786.
  • 7. Fossen, T.I., S.P. Berge: Nonlinear vectorial backstepping design for global exponential tracking of marine vessels in the presence of actuator dynamics, in: Proceedings of the 36th IEEE Conference on Decision and Control, IEEE, 1998, pp. 4237–4242.
  • 8. Kang Y., Li D., Lao D.: Performance Robustness Comparison of Active Disturbance Rejection Control and Adaptive Backstepping Sliding Mode Control. In: Xiao T., Zhang L., Fei M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 324. Springer, Berlin, Heidelberg
  • 9. Katebi, M.R., M.J. Grimble, Y. Zhang: H∞ robust control design of dynamic ship positioning, IEE Process Control Theory Application. 144, 1997, pp. 110–120.
  • 10. Krstić, M., I. Kanellakopoulos, P. Kokotović: Nonlinear and adaptive control design, Wiley 1995.
  • 11. Kuczkowski Ł., Śmierzchalski R. (2017) Path planning algorithm for ship collisions avoidance in environment with changing strategy of dynamic obstacles. In: Mitkowski W., Kacprzyk J., Oprzędkiewicz K., Skruch P. (eds) Trends in Advanced Intelligent Control, Optimization and Automation. KKA 2017. Advances in Intelligent Systems and Computing, vol 577. Springer, Cham, pp. 641–650.
  • 12. Kwan, C., F.L. Lewis: Robust backstepping control of nonlinear systems using neural networks, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions Vol. 30, No.6, 2000, pp. 753–766.
  • 13. Linkens, D.A., Mahfouf, M. Abood, M.: Self-adaptive and self-organising control applied to nonlinear multivariable anesthesia: a comparative model-based study, IEE Proceedings-D, vol. 139, No. 4, July 1992, pp. 381-394
  • 14 . Lisowski, J.: Game control methods in avoidance of ships collisions, Polish Maritime Research, No. 19, 2012, pp. 3–10.
  • 15. Lisowski, J., A. Lazarowska: The radar data transmission to computer support system of ship safety, Solid State Phenomena. 196, 2013, pp. 95–101.
  • 16. Mingyu, F., X. Yujie, Z. Li: Bio-inspired Trajectory Tracking Algorithm for Dynamic Positioning Ship with System Uncertainties, Proceedings of the 35th Chinese Control Conference, 2016, pp. 4562–4566.
  • 17. Niksa-Rynkiewicz,T.,Szłapczyński R.: A framework of a ship domain – based near-miss detection method using Mamdani neuro-fuzzy classification, Polish Maritime Research, SI (97), 2018, vol. 25, pp. 14-21.
  • 18. Orr, M.J.L.: Introduction to Radial Basis Function Networks,1996.
  • 19. Sorensen, A.: A survey of dynamic positioning control systems, Annual Reviews in Control, 35, 2011, pp. 123–136.
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  • 21. Szczypta J., Przybył A., Cpałka K. (2013) Some Aspects of Evolutionary Designing Optimal Controllers. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science, vol 7895. Springer, Berlin, Heidelberg.
  • 22. Szlapczynski, R., J. Szlapczynska: Customized crossover in evolutionary sets of safe ship trajectories, International Journal of Applied Mathematics and Computer Science. 22, 2012.
  • 23. Tannuri, E.A., A.C. Agostinho, H.M. Morishita, L. Moratelli: Dynamic positioning systems: An experimental analysis of sliding mode control, Control Engineering Practice. 18, 2010, pp. 1121–1132.
  • 24. Witkowska, A., R. Śmierzchalski: Adaptive Dynamic Control Allocation for Dynamic Positioning of Marine Vessel Based on Backstepping Method and Sequential Quadratic Programming. Ocean Engineering 163, 2018, pp. 570-582.
  • 25. Witkowska, A., T. Niksa-Rynkiewicz: Motion control of dynamically positioned unit by using backstepping method and artificial neural networks, to be published in Polish Maritime Research.
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Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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