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“Identification of Dynamically Positioned Ships”

Authors: Thor I. Fossen, Svein I. Sagatun and Asgeir J. Sørensen,
Affiliation: ABB and NTNU, Department of Engineering Cybernetics
Reference: 1996, Vol 17, No 2, pp. 153-165.

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Keywords: Dynamic positioning, identification, full-scale sea trials, Kalman filtering, self-tuning control, marine systems

Abstract: Todays model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to use linear quadratic optical control theory. However, it is difficult to identify the mathematical model of a dynamically posititmed (DP) ship since the ship is not persistently excited under DP. In addition the ship parameter estimation problem is nonlinear and multivariable with only position and thruster state measurements available for parameter estimation. The process and measurement noise must also be modeled in order to avoid parameter drift due to environmental disturbances and sensor failure. This article discusses an off-line parallel extended Kalman filter (EKF) algorithm utilizing two measurement series in parallel to estimate the parameters in the DP ship model. Full-scale experiments with a supply vessel are used to demonstrate the convergence and robustness of the proposed parameter estimator.

PDF PDF (1373 Kb)        DOI: 10.4173/mic.1996.2.7



DOI forward links to this article:
  [1] David Moreno-Salinas, Dictino Chaos, Eva Besada-Portas, José Antonio López-Orozco, Jesús M. de la Cruz and Joaquín Aranda (2013), doi:10.1155/2013/890120
  [2] David Moreno-Salinas, Dictino Chaos, Jesús Manuel de la Cruz and Joaquín Aranda (2013), doi:10.1155/2013/803548
  [3] Jesús M. de la Cruz García, Joaquín Aranda Almansa and José M. Girón Sierra (2012), doi:10.1016/j.riai.2012.05.001
  [4] D. Moreno-Salinas, E. Besada-Portas, J.A. López-Orozco, D. Chaos, J.M. de la Cruz and J. Aranda (2015), doi:10.1016/j.ifacol.2015.10.282


References:
[1] ABKOWITZ., M. A. (1975). System identification techniques for ship maneuvering trials, In: Proceedings of Symposium on Control Theory and Navy Applications. Monterey, CA. pp. 337-393.
[2] ABKOWITZ, M. A. (1980). Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification, In: Transactions on SNAME, 88,283-318.
[3] BALCHEN, J.G., JENSSEN, N.A., SÆLID, S. (1976). Dynamic positioning using Kalman filtering and optimal control theory, In: IFACIIFIP Symposium on Automation in Offshore Oil Field Operation. Holland, Amsterdam. pp. 183-186.
[4] BALCHEN, J. G., JENSSEN, N. A., SÆLID, S. (1980). Dynamic positioning of floating vessels based on Kalman filtering and optimal control, In: Proceedings of the 19th IEEE Conference on Decision and Control. New York, NY. pp. 852-864.
[5] BALCHEN, J.G., JENSSEN, N.A., MATHISEN, E., SÆLID, S. (1980). Dynamic positioning system based on Kalman filtering and optimal control, Modeling, Identification and Control, 1, 135-163 doi:10.4173/mic.1980.3.1
[6] FALTINSEN, O. M. (1990). Sea Loads on Ships and Offshore Structures, Cambridge University Press.
[7] FOSSEN, T. I. (1994). Guidance and Control of Ocean Vehicles, John Wiley and Sons Ltd.
[8] FUNG, P.T.-K., GRIMBLE, M.J. (1983). Dynamic ship positioning using a self tuning Kalman filter, IEEE Transactions on Automatic Control, 28,339-349 doi:10.1109/TAC.1983.1103226
[9] GELB, A., KASPER, J.F., JR., NASH, R.A., JR., PRICE, C.F., SUTHERLAND, A.A., JR. (1988). Applied Optimal Estimation, MIT Press, Boston, Massachusetts.
[10] GRIMBLE, M.J., PATTON, R.J., WISE, D.A. (1980). The design of dynamic positioning control systems using stochastic optimal control theory, Optimal Control Applications and Methods, 1, 167-202 doi:10.1002/oca.4660010207
[11] GRIMBLE, M.J., PATTON, R.J., WISE, D.A. (1980). Use of Kalman filtering techniques in dynamic ship positioning systems, In: IEE Proceedings, 127, D, 93-102.
[12] HWANG, WEI-YUAN (1980). Application of System Identification to Ship Maneuvering, Master´s thesis. Massachusetts Institute of Technology.
[13] SÆLID, S., JENSSEN, N.A., BALCHEN, J.G. (1983). Design and analysis of a dynamic positioning system based on Kalman filtering and optimal control, IEEE Transaction on Automatic Control, 28, 331-339 doi:10.1109/TAC.1983.1103225
[14] SØRENSEN, A., SAGATUN, S.I., FOSSEN, T.I. (1995). The design of a dynamic positioning system using model based control, In: Preprints IFAC Workshop on Control Applications in Marine Systems.CAMS ´95. Trondheim, Norway.


BibTeX:
@article{MIC-1996-2-7,
  title={{Identification of Dynamically Positioned Ships}},
  author={Fossen, Thor I. and Sagatun, Svein I. and Sørensen, Asgeir J.},
  journal={Modeling, Identification and Control},
  volume={17},
  number={2},
  pages={153--165},
  year={1996},
  doi={10.4173/mic.1996.2.7},
  publisher={Norwegian Society of Automatic Control}
};

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