Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method

Abstract In order to accurately identify the ship's roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares - Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms' weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well.

[1]  Zao-Jian Zou,et al.  Online prediction of ship roll motion based on a coarse and fine tuning fixed grid wavelet network , 2018, Ocean Engineering.

[2]  G. Miao,et al.  NON-PARAMETRIC IDENTIFICATION OF NONLINEAR DAMPING AND RESTORING FORCES FOR SHIP FREE ROLL DECAY IN NUMERICAL TANK , 2017 .

[3]  Axel Hahn,et al.  Comparison and optimization of the parameter identification technique for estimating ship response models , 2017, 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE).

[4]  Abhilash Somayajula,et al.  Application of advanced system identification technique to extract roll damping from model tests in order to accurately predict roll motions , 2017 .

[5]  Man Zhu,et al.  Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm , 2017 .

[6]  C. Guedes Soares,et al.  Vector Field Path Following for Surface Marine Vessel and Parameter Identification Based on LS-SVM , 2016 .

[7]  Zao-jian Zou,et al.  Parameter identification of nonlinear roll motion equation for floating structures in irregular waves , 2016 .

[8]  Marc Vantorre,et al.  The towing tank for manoeuvres in shallow water , 2016 .

[9]  Roll damping decay of a FPSO with bilge keel , 2013 .

[10]  Ingo Steinwart,et al.  Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs , 2012 .

[12]  Yoshiho Ikeda,et al.  A Simple Prediction Formula of Roll Damping of Conventional Cargo Ships on the Basis of Ikeda's Method and Its Limitation , 2011 .

[13]  Norio Tanaka,et al.  Components of roll damping of ship at forward speed , 1978 .

[14]  Taek Soo Jang,et al.  Recovering the functional form of the nonlinear roll damping of ships from a free-roll decay experiment: An inverse formulism , 2010 .

[15]  J. Suykens,et al.  Parameter Estimation for Time Varying Dynamical Systems using Least Squares Support Vector Machines , 2012 .

[16]  Zao-Jian Zou,et al.  SVR-based identification of nonlinear roll motion equation for FPSOs in regular waves , 2015 .

[17]  Abhilash Somayajula,et al.  An overview of the prediction methods for roll damping of ships , 2015 .

[18]  Norio Tanaka,et al.  On Eddy Making Component of Roll Damping Force on Naked Hull , 1977 .

[19]  K. Kannan,et al.  Parameter identification for nonlinear damping coefficient from large-amplitude ship roll motion using wavelets , 2017 .

[20]  Wu Ming,et al.  Numerical simulation of naval ship's roll damping based on CFD , 2012 .

[21]  Anastassios N. Perakis,et al.  A real-time ship roll motion prediction using wavelet transform and variable RBF network , 2018, Ocean Engineering.

[22]  Qiang Zhang,et al.  Linear reduction of backstepping algorithm based on nonlinear decoration for ship course-keeping control system , 2018 .

[23]  Ayman B. Mahfouz.  Identification of the nonlinear ship rolling motion equation using the measured response at sea , 2004 .

[24]  Liping Sun,et al.  Parameter Identification of the Non-Linear Rolling Damping Based on PLS Regression Technique , 2013 .

[25]  Yoji Himeno,et al.  Prediction of Ship Roll Damping. A State of the Art , 1981 .

[26]  Metin Kemal Gokce,et al.  Numerical simulations of free roll decay of DTMB 5415 , 2018, Ocean Engineering.

[27]  L. McCue,et al.  Modeling Ship Equations of Roll Motion Using Neural Networks , 2010 .

[28]  Marc Vantorre,et al.  Manoeuvring model of a container vessel in coastal waves , 2018 .

[29]  Zao-Jian Zou,et al.  On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks , 2013 .

[30]  Paisan Atsavapranee,et al.  Experimental Investigation of Viscous Roll Damping on the DTMB Model 5617 Hull Form , 2007 .

[31]  Antonio Carlos Fernandes,et al.  The Nonlinear Roll Damping of a FPSO Hull , 2014 .

[33]  Yooil Kim,et al.  Identification of the nonlinear roll damping and restoring moment of a FPSO using Hilbert transform , 2015 .

[34]  Moustafa Abdel-Maksoud,et al.  Estimation of ship roll damping—A comparison of the decay and the harmonic excited roll motion technique for a post panamax container ship , 2016 .

[35]  Joaquín Aranda Almansa,et al.  Identification of a Surface Marine Vessel Using LS-SVM , 2013, J. Appl. Math..