A comparative and experimental study on gradient and genetic optimization algorithms for parameter identification of linear MIMO models of a drilling vessel

Stanisław Bańka ; Michał Brasel ; Paweł Dworak ; Krzysztof Jaroszewski

International Journal of Applied Mathematics and Computer Science, Tome 25 (2015), p. 877-893 / Harvested from The Polish Digital Mathematics Library

Résumé

The paper presents algorithms for parameter identification of linear vessel models being in force for the current operating point of a ship. Advantages and disadvantages of gradient and genetic algorithms in identifying the model parameters are discussed. The study is supported by presentation of identification results for a nonlinear model of a drilling vessel.

Détail

Publié le : 2015-01-01
EUDML-ID : urn:eudml:doc:275932

Bibliographie

[000] Al Seyab, R. and Cao, Y. (2008). Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation, Journal of Process Control 18(6): 568-581.

[001] Anil Ari Guner, H., Yuksel, Y. and Cevik, E.O. (2013). Estimation of wave parameters based on nearshore wind-wave correlations, Ocean Engineering 63: 52-62.

[002] Aström, K. and Källström, C. (1976). Identification of ship steering dynamics, Automatica 12(1): 9-22.

[003] Aström, K. and Källström, C. (1981). Experiences of system identification applied to ship steering, Automatica 17(1): 187-198.

[004] 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).

[005] Bańka, S., Dworak, P., Brasel, M. and Latawiec, K.J. (2010). 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.

[006] 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. | Zbl 1293.93428

[007] Barth, S. and Eecen, P. (2006). Description of the relation of wind, wave and current characteristics at the offshore wind farm Egmond aan Zee, Technical report, Noordzee Wind, IJmuiden.

[008] Billings, S. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-temporal Domains, Wiley, Chichester. | Zbl 1287.93101

[009] Bredmose, H., Larsen, S., Matha, D., Rettenmeier, A., Marino, E. and Seattran, L. (2012). Collation of offshore wind-wave dynamics, Technical report, Marine Renewables Infrastructure Network.

[010] Chen, W., Yan, K., Shi, G. and Lu, H. (2008). A new swarm intelligence and parameter identification of hydrodynamic forces acting on axis-symmetric body exiting water obliquely, Journal of Ship Mechanics 12(2): 204-210.

[011] Dai, Y., Li, Y. and Song, J. (2012). Parameter identification of ship lateral motions using evolution particle swarm optimization, Proceedings of the 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012, Harbin, China, pp. 797-801.

[012] Dai, Y., Zhao, X. and Liu, L. (2010). Parameter identification of ship longitudinal motions using continuous ant colony algorithm with period searching, Journal of Ship Mechanics 14(8): 872-878.

[013] Dworak, P. and Brasel, M. (2013). Improving quality of regulation of a nonlinear MIMO dynamic plant, Elektronika Ir Elektrotechnika 19(7): 3-6.

[014] Fossen, T.I. (1994). Guidance and Control of Ocean Vehicles, John Wiley and Sons, Chichester.

[015] Fossen, T.I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley and Sons, Chichester.

[016] Herrero, E. and González, F.V. (2012). Two-step identification of non-linear manoeuvring models of marine vessels, Ocean Engineering 53: 72-82.

[017] Kondo, J., Fujinawa, Y. and Naito, G. (1972). Wave-induced wind fluctuation over the sea, Journal of Fluid Mechanics 51(4): 751-771.

[018] Lewis, E.V. (1989). Principles of Naval Architecture, Volume III: Motions in Waves and Controllability, Society of Naval Architects and Marine Engineers, Alexandria, VA.

[019] Ljung, L. (2001). System Identification. Theory for the User, Prentice-Hall, Englewood Cliffs, NJ. | Zbl 0615.93004

[020] Mahfouz, A. and Haddara, M. (2003). Effect of the damping and excitation on the identification of the hydronamic parameters for an underwater robotic vehicle, Ocean Engineering 30(8): 1005-1025.

[021] Mzyk, G. (2013). Nonparametric instrumental variables for identification of block-oriented systems, International Journal of Applied Mathematics and Computer Science 23(3): 521-537, DOI: 10.2478/amcs-2013-0040. | Zbl 1279.93103

[022] Nelles, O. (2001). Nonlinear System Identification, Springer-Verlag, Berlin/Heidelberg. | Zbl 0963.93001

[023] Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2013). Nonlinear system identification using heterogeneous multiple models, International Journal of Applied Mathematics and Computer Science 23(1): 103-115, DOI: 10.2478/amcs-2013-0009. | Zbl 1293.93217

[024] Padilla, A. and Yuz, J. (2013). Continuous-time system identification of a ship on a river, Proceedings of the 52nd Annual Conference on Decision and Control, CDC 2013, Firenze, Italy, pp. 4553-4558.

[025] Revestido, E., Velasco, F., Zamanillo, I., Lopez, E. and Moyano, E. (2011). Parameter estimation of ship linear maneuvering models, Proceedings of IEEE OCEANS 2011, Santander, Spain, pp. 1-8.

[026] Skjetne, R., Smogeli, Ø. and Fossen, T. (2004). A nonlinear ship manoeuvering model: Identification and adaptive control with experiments for a model ship, Modeling, Identification and Control 25(1): 3-27.

[027] Tran Khanh, T., Ouahsine, A., Naceur, H. and Wassifi, K.E. (2013). Assessment of ship manoeuvrability by using a coupling between a nonlinear transient manoeuvring model and mathematical programming techniques, Journal of Hydrodynamics B 25(5): 788-804.

[028] Velasco, F., Herrero, E., Lopez, L. and Moyano, E. (2013). Identification for a heading autopilot of an autonomous in-scale fast ferry, IEEE Journal of Oceanic Engineering 38(2): 263-274.

[029] Wang, Y., Meng, H., Liu, Z.-L. and Yu, R.-T. (2011). On-line of modeling ship manoeuvring motion by using least squares support vector machines, Proceedings of the IEEE 18th International Conference on Industrial Engineering and Engineering Management, Changchun, China, pp. 1712-1716.

[030] 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.

[031] Yoon, H. and Rhee, K. (2003). Identification of hydrodynamic coefficients in ship maneuvering equations of motion by estimation-before-modeling technique, Ocean Engineering 30(18): 2379-2404.

[032] Yoon, H., Son, N.S. and Lee, C.M. (2004). Estimation of roll related hydrodynamic coefficients through the free running model tests, Proceedings of IEEE TECHNO-OCEAN'04, Kobe, Japan, pp. 1086-1092.

[033] Yoon, H., Son, N. and Lee, G. (2007). Estimation of the roll hydrodynamic moment model of a ship by using the system identification method and the free running model test, IEEE Journal of Oceanic Engineering 32(4): 798-806.

[034] Zhang, H.-G. and Zou, Z.-J. (2011). Identification of abkowitz model for ship manoeuvring motion using ε-support vector regression, Journal of Hydrodynamics B 23(3): 353-360.

[035] Zhang, Z., Li, X., Yang, L. and Wu, G.Y. (2010). Parameter identification method for ship swaying motion differential equations, Proceedings of the 3rd International Joint Conference on Computational Science and Optimization, CSO 2010, Huangshan, Anhui, China, pp. 205-208.