Autonomous Ship Utility Model Parameter Estimation Utilising Extended Kalman Filter

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)


In this paper, a problem of autonomous ship utility model identification for control purposes is considered. In particular, the problem is formulated in terms of model parameter estimation (one-step-ahead prediction). This is a complex task due to lack of measurements of the parameter values, their time-variability and structural uncertainty introduced by the available models. In this work, authors consider and compare two utility models based on often utilised ship model structures with time-varying parameters identified recursively using the extended Kalman lter (EKF). The validation results have been obtained using simulation experiments in which the required information for the parameter estimation task had been generated using a cognitive model of B-481 ship. The results indicate the benefits and drawbacks, in terms of estimation accuracy and computational complexity, of using each of the investigated utility model structures.


Extended Kalman filter Ship parameter identification Estimation 


  1. 1.
    Arminski, K., Zubowicz, T.: Robust identification of Quadrocopter model for control purposes. In: 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 337–342. IEEE (2017)Google Scholar
  2. 2.
    Burgers, G., Jan van Leeuwen, P., Evensen, G.: Analysis scheme in the ensemble Kalman filter. Monthly Weather Rev. 126(6), 1719–1724 (1998).<1719:ASITEK>2.0.CO;2
  3. 3.
    Daum, F.: Nonlinear filters: beyond the Kalman filter. IEEE A&E Aerosp. Electron. Syst. Mag. 20, 57–69 (2005). Scholar
  4. 4.
    Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley (2011)Google Scholar
  5. 5.
    Galbas, J.: Synteza układu sterowania precyzyjnego statkiem za pomoca̧ sterów strumieniowych. Ph.D. thesis, Gdańsk University of Technology (1998). (in Polish)Google Scholar
  6. 6.
    Haro, M.: Stabilization of the ship’s dynamic by the use of a dynamic controller. In: XXIII Automatic Journeys of the CEA-IFAC, pp. 157–168. Tenerife, Spain (2002)Google Scholar
  7. 7.
    Jaroś, K., Witkowska, A., Śmierzchalski, R.: Designing particle kalman filter for dynamic positioning. In: Kościelny, J.M., Syfert, M., Sztyber, A. (eds.) Advanced Solutions in Diagnostics and Fault Tolerant Control, pp. 157–168. Springer, Cham (2018)CrossRefGoogle Scholar
  8. 8.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960). Scholar
  9. 9.
    Khazraj, H., Faria da Silva, F., Bak, C.: A performance comparison between extended Kalman filter and unscented kalman filter in power system state estimation. In: Proceedings of the 2016 51st International Universities’ Power Engineering Conference (2016).
  10. 10.
    Lazarowska, A.: Ship’s trajectory planning for collision avoidance at sea based on ant colony optimisation. J. Navig. 68, 291–307 (2015). Scholar
  11. 11.
    Shi, C., Zhao, D., Peng, J., Shen, C.: Identification of ship maneuvering model using extended Kalman filtering. In: Marine Navigation and Safety of Sea Transportation, vol. 3, pp. 329–334 (2009).
  12. 12.
    Śmierzchalski, R.: Automatyzacja i sterowania statkiem. Wydawnictwo Politechniki Gdańskiej (2013)Google Scholar
  13. 13.
    Tomera, M.: Hybrid switching controller design for the maneuvering and transit of a training ship. Int. J. Appl. Math. Comput. Sci. 27(1), 63–77 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Witkowska, A., Śmierzchalski, R.: Adaptive backstepping tracking control for an over-actuated DP marine vessel with inertia uncertainties. Int. J. Appl. Math. Comput. Sci. 28(4), 679–693 (2018)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Xie, S., Chu, X., Liu, C., Liu, J., Mou, J.: Parameter identification of ship motion model based on multi-innovation methods. J. Marine Sci. Technol. (2019). Scholar
  16. 16.
    Zubowicz, T., Arminski, K., Witkowska, A., Śmierzchalski, R.: Marine autonomous surface ship - control system configuration. IFAC-PapersOnLine 52(8), 409–415 (2019). 10th IFAC Symposium on Intelligent Autonomous Vehicles IAV 2019CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Automatic ControlGdańsk University of TechnologyGdańskPoland
  2. 2.Department of Electrical Engineering, Control Systems and InformaticsGdańsk University of TechnologyGdańskPoland

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