Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
Rocznik
Tom
Strony
23--29
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
- Carl-von-Ossietzky University of Oldenburg, Oldenburg, Germany
autor
- Carl-von-Ossietzky University of Oldenburg, Oldenburg, Germany
autor
- School of Navigation, Wuhan University of Technology, Hubei, China
autor
- Institute of Information Technology, Oldenburg, Germany
Bibliografia
- 1 Skjetne, S., Smogeli, V., & Fossen, T. I. 2004. Modeling, identification, and adaptive maneuvering of Cybership II: A complete design with experiments. IFAC Conference on Control & Application in Marine System (pp.203‐208). Ancona, Italy.
- 2 Xu, F., Xiao, T., et al. 2014. Identification of Nomoto models with integral sample structure for identification. The 33rd Chinese Control Conference (pp.6721‐6725). Nanjing, China
- 3 Åstrom, K., & Kållstrom, C. 1976. Identification of ship steering dynamics. Automatica, 9‐22.
- 4 Barford, L. A., Fazzio, R. S., & Smith, D. R. 1992. An introduction to wavelets. Hewlett‐Packard Labs, Bristol, UK, Tech. Rep. HPL‐92‐124, 1–29.
- 5 Fossen, T. I. 2011. Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley.
- 6 Jiang, Z., Yan, W., Jin, X., & Gao, J. 2012. Identification of an Underactuated Unmanned Surface Vehicle. JOURNAL OF NORTHWESTERN POLYTECHNICAL UNIVERSITY, 699‐705.
- 7 Ljung, L. 2002. Recursive identification algorithms. Circuits, Systems, and Signal Processing, 57‐68.
- 8 Luo, W. L., & Zou, Z. J. 2009. Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines. Journal of Ship Research, 19‐30.
- 9 Luo, W., & Cai, W. 2014. Modeling of ship maneuvering motion using optimized support vector machines. Intelligent Control and Information Processing (ICICIP), 2014 Fifth International Conference on (pp. 476 ‐ 478). Dalian: IEEE.
- 10 Perera, L. P., Oliveira, P., & Guedes Soares, C. 2015. System Identification of Nonlinear Vessel Steering. Journal of Offshore Mechanics and Arctic Engineering, 31302‐31307.
- 11 Rajesh, G., & Bhattacharyya, S. K. 2008. System identification for nonlinear maneuvering of large tankers using artificial neural network. Applied Ocean Research, 256‐263.
- 12 Sutulo, S., & Guedes, Soares, C. 2014. An algorithm for offline identification of ship manoeuvring mathematical models from free‐running tests. Ocean Engineering, 10‐25.
- 13 Shi, C., Zhao, D., Peng, J., & Shen, C. 2009. Identification of Ship Maneuvering Model Using Extended Kalman Filters. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 105‐110.
- 14 Vapnik, V. 2000. The nature of statistical learning theory. New York: Springer.
- 15 Wang, X., Zou, Z. J., & Yin, J. 2013. Modular Parameter Identification for Ship Manoeuvring Prediction Based on Support Vector Machines. The Twenty‐third (2013) International Offshore and Polar Engineering (pp. 834‐839). Anchorage, Alaska, USA: the International Society of Offshore and Polar Engineers
- 16 Wang, Y.‐H., Yeh, C.‐H., Young, H.‐W. V., Hu, K., & Lo, M.T. 2014.On the computational complexity of the empirical mode decomposition algorithm. Physical A: Statistical Mechanics and its Applications, 159‐167.
- 17 XU, F., CHEN, Q., ZOU, Z.‐J., & YIN, J.‐C. 2014. Modeling of underwater vehicles’ maneuvering motion by using integral sample structure for identification. JOURNAL OF SHIP MECHANICS, 211‐220.
- 18 Xu, F., Zou, Z. J., Yin, J. C., & Cao, J. 2012. Parametric identification and sensitivity analysis for Autonomous Underwater Vehicles in diving plane. Journal of Hydrodynamics, 744‐751.
- 19 Zhang, X. G., & Zou, Z. J. 2011. Identification of Abkowitz model for ship manoeuvring motion using ε‐support vector regression. Journal of Hydrodynamics, 353‐360.
- 20 Zhang, X.‐G., & Zou, Z.‐J. 2013. Estimation of the hydrodynamic coefficients from captive model test results by using support vector machines. Ocean Engineering, 25‐31.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a668c60a-df63-4409-81c5-b2c40e1540c4