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

• Autorzy: Stanisław Bańka , Paweł Dworak , Krzysztof Jaroszewski
• 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.

Słowa kluczowe

EN

MIMO multivariable control systems; nonlinear systems; neural control

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 2
• Strony: 357-369

Daty

wydano

2014

otrzymano

2013-04-29

poprawiono

2013-12-20

poprawiono

2014-02-21

Twórcy

autor

Stanisław Bańka

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

autor

Paweł Dworak

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

autor

Krzysztof Jaroszewski

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

Bibliografia

• Akesson, B. and Tojvonen, H. (2006). A neural network model predictive controller, Journal of Process Control 16(9): 937-946.
• 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.
• Qin, S. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764.
• Rawlings, J. and Mayne, D. (2009). Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison, WI.
• Shevitz, D. and Paden, B. (1994). Lyapunov stability theory of nonsmooth systems, IEEE Transactions on Automatic Control 39(9): 1910-1914.
• Tatjewski, P. (2007). Advanced Control of Industrial Processes, Springer-Verlag, London.
• Tomera, M. (2010). Nonlinear controller design of a ship autopilot, International Journal of Applied Mathematics and Computer Science 20(2): 271-280, DOI: 10.2478/v10006-010-0020-8.
• Tzirkel-Hancock, E. and Fallside, F. (1992). Stable control of nonlinear systems using neural networks, International Journal of Robust and Nonlinear Control 2(1): 63-86.
• van der Boom, T., Botto, M. and Hoekstra, P. (2005). Design of an analytic constrained predictive controller using neural networks, International Journal of Systems Science 36(10): 639-650.
• 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.
• Zhai, G. and Xu, X. (2010). A unified approach to stability analysis of switched linear descriptor systems under arbitrary switching, International Journal of Applied Mathematics and Computer Science 20(2): 249-259, DOI: 10.2478/v10006-010-0018-2.

Identyfikatory

DOI

10.2478/amcs-2014-0027