Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | Vol. 8, no. 1 | 41--47
Tytuł artykułu

Multivariable Adaptive Controller for the Nonlinear MIMO Model of a Container Ship

Treść / Zawartość
Warianty tytułu
Języki publikacji
The paper presents an adaptive multivariable control system for a Multi-Input, Multi-Output (MIMO) nonlinear dynamic process. The problems under study are exemplified by a synthesis of a course angle and forward speed control system for the nonlinear four-Degrees-of-Freedom (4-DoF) mathematical model of a single-screw, high-speed container ship. The paper presents the complexity of the assumed model to be analyzed and a synthesis method for the multivariable adaptive modal controller. Due to a strongly nonlinear nature of the ship movements equations a multivariable adaptive controller is tuned in relation to changeable hydrodynamic operating conditions of the ship. In accordance with the given operating conditions controller parameters are chosen on the basis of four measured auxiliary signals. The system synthesis is carried out by linearization of the nonlinear model of the ship at its nominal operating points in the steady-state and by means of a pole placement control method. The final part of the paper includes results of simulation tests of the proposed control system carried out in the MATLAB/Simulink environment along with conclusions and final remarks.

Opis fizyczny
Bibliogr. 29 poz., rys.
  • Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland
  • Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland
  • 1 Akesson, B. & Tojvonen, H. (2006). A neural network model predictive controller. Journal of Process Control, 16,937–946.
  • 2 Äström, K. & Wittenmark, B. (1995). Adaptive control Addison Wesely.
  • 3 Bańka, S., Brasel, M., Dworak, P., & Latawiec, J. K. (2010a). Switched‐structure of linear MIMO controllers for positioning of a drillship on a sea surface, Międzyzdroje: Methods and Models in Automation and Robitics 2010.
  • 4 Bańka, S., Dworak, P., & Brasel, M. (2010b). On control of nonlinear dynamic MIMO plants using a switchable structure of linear modal controllers (in Polish). Pomiary, Automatyka, Kontrol, 5, 385‐391.
  • 5 Bańka, S., Dworak, P., & Jaroszewski K. (2013). Linear adaptive structure for control of a nonlinear MIMO dynamic plant. International Journal of Applied Mathematics and Computer Science 23(1), (in printing)
  • 6 Dworak, P. & Pietrusewicz, K. (2010). A variable structure controller for the MIMO Thermal Plant (in Polish). Przeglad Elektrotechniczny 6, 116‐119.
  • 7 Dworak, P. & Bańka, S. (2012a). Adaptive multi‐controller TSK Fuzzy Structure for Control of Nonlinear MIMO Dynamic Plant. 9th IFAC Conference on Manoeuvring and Control of Marine Craft.
  • 8 Dworak, P., Jaroszewski K. & Brasel. M. (2012b). A fuzzy TSK controller for the MIMO Thermal Plant (in Polish). Przeglad Elektrotechniczny 10a, 83‐86.
  • 9. Fabri, S. & Kadrikamanathan, V. (2001). Functional adaptive control. An intelligent systems approach. Springer Verlag. Berlin.
  • 10 Fossen T. I. (1994). Guidance and Control of Ocean Vehicles. John Wiley and Sons,1994.
  • 11 Gierusz, W. (2005). Synthesis of multivariable control systems for precise steering of shipʹs motion using selected robust systems design methods (in Polish). Gdynia Maritime Academy Press. Gdynia.
  • 12 Huba, M., Skogestad, S., Fikar, M., Hovd, M., Johansen, T.A., & Rohalʹ‐Ilkiv, B. (2011). Selected topics on constrained and nonlinear control. Slovakia, ROSA. Dolný Kubín.
  • 13 Ioannou P. and Sun J., 1996, Robust adaptive control: Prentice Hall, 1996.
  • 14 Khalil, H.K. (2001). Nonlinear systems. Prentice Hall.
  • 15 Lawrynczuk, M. (2010). Explicite neural network‐based nonlinear predictive control with low computational complexity. Lecture Notes in Computer Science, 6086, 649–658.
  • 16 Limon, D., Alamo, T. & Camacho, E. (2005). Enlarging the domain of attraction of mpc controllers. Automatica, 41(4), 629–635.
  • 17 Maciejowski, J. (2002). Predictive control with constraints. Prentice Hall, Engelewood Cliffs.
  • 18 Paden, B., Sastry, S.S. (1984). A calculus for computing Filippovʹs differential inclusion with application to the variable structure control of robot manipulators. IEEE Transactions on Circutts and Systems, 34(1), 73‐82.
  • 19 Qin, S. & Badgwell, T. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7), 733–764.
  • 20 Rawlings, J. & Mayne, D. (2009). Model predictive control: Theory and design. Nob Hill Publishing, Madison.
  • 21 Shevitz, D., Paden, B. (1994). Lapunov stability theory of nonsmooth systems. IEEE Transactions on Automatic Control, 39(9), 1910‐1914.
  • 22 Son, K. H., Nomoto K., 1981. On the Coupled Motion of Steering and Rolling of a High Speed Container, J.S.N.A., Japan, Vol. 150, 232‐244.
  • 23 Tanaka, K. & Sugeno, M. (1992). Stability analysis and design of fuzzy control systems. Fuzzy Sets and System 45, 135‐156.
  • 24 Tatjewski, P. (2007). Advanced Control of Industrial Processes. Springer Verlag. London.
  • 25 Tzirkel‐Hancock, E. & Fallside, F. (1992). Stable control of nonlinear systems using neural networks. International Journal of Robust and Nonlinear Control 2(1), 63‐86.
  • 26 Van Amerongen, J., 1982. Adaptive Steering of Ships – A Model Reference Aproach to Improved Maneuvering and Economical Course Keeping, PhD thesis, Delf University of Technology, The Netherlands, 1982.
  • 27 van der Boom, T., Botto, M. & Hoekstra, P. (2005). Design of an analytic constrained predictive controller using neural networks. International Journal of Systems Science, 36(10), 639–650.
  • 28 Vidyasagar, M. (1985). Control system synthesis: A factorization approach. The Massachusetts Institute of Technology Press. Massachusetts.
  • 29 Witkowska, A., Tomera, M., & Śmierzchalski R. (2007). A backstepping approach to ship course control. International Journal of Applied Mathematics and Computer Science, 17(1), 73‐85.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.