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2014 | 24 | 2 | 357-369

Tytuł artykułu

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship's current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.

Rocznik

Tom

24

Numer

2

Strony

357-369

Opis fizyczny

Daty

wydano
2014
otrzymano
2013-04-29
poprawiono
2013-12-20
poprawiono
2014-02-21

Twórcy

  • Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, Poland
  • Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, Poland
  • Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, Poland

Bibliografia

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  • Åström, K. and Wittenmark, B. (1995). Adaptive Control, Addison Weseley, Reading.
  • Bańka, S. (2007). Multivariable Control Systems: A Polynomial Approach, Monographs of the Committee of Automation and Robotics, Polish Academy of Sciences, Szczecin University of Technology Press, Szczecin, (in Polish).
  • Bańka, S. (2012). On methods of modal controller synthesis in MIMO systems, in K. Malinowski and M. Busłowicz (Eds.), Advances in Control Theory and Automation, Printing House of Białystok University of Technology, Białystok, pp. 35-46.
  • Bańka, S., Dworak, P. and Brasel, M. (2010a). On control of nonlinear dynamic MIMO plants using a switchable structure of linear modal controllers, Pomiary, Automatyka, Kontrola 56(5): 385-391, (in Polish).
  • Bańka, S., Dworak, P., Brasel, M. and Latawiec, K.J. (2010b). A switched structure of linear MIMO controllers for positioning of a drillship on a sea surface, Proceedings of the 15th International Conference on Methods and Models in Automation and Robotics, MMAR 2010, Międzyzdroje, Poland, pp. 249-254.
  • Bańka, S., Dworak, P. and Jaroszewski, K. (2011). Problems associated with realization of neural modal controllers designed to control multivariable dynamic systems, in K. Malinowski and R. Dindorf (Eds.), Advances of Automatics and Robotics, Kielce University of Technology Press, Kielce, pp. 27-41, (in Polish).
  • Bańka, S., Dworak, P. and Jaroszewski, K. (2013). Linear adaptive structure for control of a nonlinear MIMO dynamic plant, International Journal of Applied Mathematics and Computer Science 23(1): 47-63, DOI: 10.2478/amcs-2013-0005.
  • Bańka, S. and Latawiec, K.J. (2009). On steady-state error-free regulation of right-invertible LTI MIMO plants, Proceedings of the 14th International Conference on Methods and Models in Automation and Robotics, MMAR 2009, Międzyzdroje, Poland, DOI: 10.3182/20090819-3-PL-3002.00066.
  • Chen, J. and Yea, Y. (2002). Neural network-based predictive control for multivariable processes, Chemical Engineering Communications 189(7): 865-894.
  • Fabri, S. and Kadrikamanathan, V. (2001). Functional Adaptive Control. An Intelligent Systems Approach, Springer-Verlag, Berlin.
  • Khalil, H.K. (2001). Nonlinear Systems, Prentice Hall, Englewood Cliffs, NJ.
  • Ławryńczuk, M. (2010). Explicit neural network-based nonlinear predictive control with low computational complexity, in M. Szczuka, M. Kryszkiewicz, S. Ramanna, R. Jensen and Q. Hu (Eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, Vol. 6086, Springer, Berlin/Heidelberg, pp. 649-658.
  • Lee, C., Shin, M. and Chung, M. (2001). A design of gain-scheduled control for a linear parameter varying system: An application to flight control, Control Engineering Practice 9(1): 11-21.
  • Limon, D., Alamo, T. and Camacho, E. (2005). Enlarging the domain of attraction of MPC controllers, Automatica 41(4): 629-635.
  • Maciejowski, J. (2002). Predictive Control with Constraints, Prentice Hall, Englewood Cliffs, NJ.
  • Pedro, J.O. and Dahunsi, O.A. (2011). Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system, International Journal of Applied Mathematics and Computer Science 21(1): 137-147, DOI: 10.2478/v10006-011-0010-5.
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  • Vidyasagar, M. (1985). Control System Synthesis: A Factorization Approach, MIT Press, Cambridge, MA.
  • Wise, D.A. and English, J.W. (1975). Tank and wind tunnel tests for a drill-ship with dynamic position control, Offshore Technology Conference, Dallas, TX, USA, pp. 103-118.
  • Witkowska, A., Tomera, M. and Śmierzchalski, R. (2007). A backstepping approach to ship course control, International Journal of Applied Mathematics and Computer Science 17(1): 73-85, DOI: 10.2478/v10006-007-0007-2.
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Bibliografia

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