Parameter Estimation of Maneuvering Variables of SIGMA-Extended War Ship Using Kalman Filter

Article Preview

Abstract:

SIGMA-class warship one of warship type is designed up to sea state 6. The ship was redesigned in enlarged dimensions, and called SIGMA extended class warships. A control system can give a performance of response in accordance to the set point when the value of controlled variable can be transmitted to the controller accurately. The characteristic of sensor is not usually able to tranmit a proper value, due to the noise on the sensor and also the environment disturbances. This paper describe a strategy of Kalman filter to estimate variables controlled when the presence noise on the compass or gyrocompas. The result of Kalman filter implementations give the magnitude of the integral absolute error of yaw and sway less than 5%, when there are noise on the measurement and the disturbances.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

96-102

Citation:

Online since:

January 2018

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2018 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] T.I. Fossen, Handbook Of Marine Craft Hydrodynamics And Motion Control, John Wiley & Sons, Norway, (2011).

Google Scholar

[2] K. Ogata, Modern Control Engineering (4th ed. ), Pearson Education International, New Jersey, (2002).

Google Scholar

[3] S. Sutulo, C.G. Soares, An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests, Ocean Engineering, 79 (2014) 10-25, doi: 10. 1016/j. oceaneng. 2014. 01. 007.

DOI: 10.1016/j.oceaneng.2014.01.007

Google Scholar

[4] X. Luo, B. Jiu, S. Chen, Q. Ge, ML estimation of transition probabilities for an unknown maneuvering emitter tracking $, Signal Processing, 109 (2015) 248–260, doi: 10. 1016/j. sigpro. 2014. 11. 004.

DOI: 10.1016/j.sigpro.2014.11.004

Google Scholar

[5] T. Dong, A.J. Sørensen, S. Tong, Design of Hybrid Controller for Dynamic Positioning from Calm to Extreme Sea Conditions, (2001).

Google Scholar

[6] M.Y. Santoso, S. Su, Nonlinear Rudder Roll Stabilization using Fuzzy Gain Scheduling - PID Controller for Naval Vessel, (2013) 94-99.

DOI: 10.1109/ifuzzy.2013.6825416

Google Scholar

[7] A.S. Aisjah, A.A. Masroeri, A. Sulisetiyono, I.I. Munadhif, Fuzzy control system for stability rolling in sigma class warship, In: Proc. ISOCEEN, Surabaya (2015) 2-4.

Google Scholar

[8] V. Ødegård, Nonlinear Identification of Ship Autopilot Models, (2009).

Google Scholar

[9] Akbar, Ridho, A.S. Aisjah, Pemodelan Kapal Perang Kelas Sigma Extended Skala 3 Meter, (2014).

Google Scholar

[10] M. D. Woodward, Evaluation of inter-facility uncertainty for ship manoeuvering performance prediction, Ocean Engineering, 88 (2014) 598–606, doi: 10. 1016/j. oceaneng. 2014. 04. 001.

DOI: 10.1016/j.oceaneng.2014.04.001

Google Scholar

[11] M. Araki, H. Sadat-hosseini, Y. Sanada, K. Tanimoto, N. Umeda, F. Stern, Estimating maneuvering coefficients using system identification methods with experimental , system-based, and CFD free-running trial data. Ocean Engineering, 51 (2012).

DOI: 10.1016/j.oceaneng.2012.05.001

Google Scholar

[12] Lewis, L. Frank, L.P.D. Xie, Optimal and Robust Estimation, (S.S. Lewis, L. Frank, Ed. Ge) (2nd ed. ), (2008).

Google Scholar

[13] M.S. Grewal, Kalman Filtering : Theory and Practice Using MATLAB California State University at Fullerton (Vol. 5), (2001).

Google Scholar

[14] T.I. Fossen, Guidance and Control of Ocean Vehicles - Thor I, Fossen. pdf (1st ed. ), British Library, Trondheim, (1994).

Google Scholar