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Ship maneuvering models are the keys to the research of ship maneuverability, design of ship motion control system and development of ship handling simulators. For various frames of ship maneuvering models, determining the parameters of the models is always a tedious task. System identification theory can be used to establish system mathematical models by the system’s input data and output data. In this paper, based on the analysis of ship hydrodynamics, a nonlinear model frame of ship maneuvering is established. System identification theory is employed to estimate the parameters of the model. An algorithm based on the extended Kalman filter theory is proposed to calculate the parameters. In order to gain the system’s input and output data, which is necessary for the parameters identification experiment, turning circle tests and Zig-zag tests are performed on shiphandling simulator and the initial data is collected. Based on the Fixed Interval Kalman Smoothing algorithm, a pre-processing algorithm is proposed to process the raw data of the tests. With this algorithm, the errors introduced during the measurement process are eliminated. Parameters identification experiments are designed to estimate the model parameters, and the ship maneuvering model parameters estimation algorithm is extended to modify the parameters being estimated. Then the model parameters and the ship maneuvering model are determined. Simulation validation was carried out to simulate the ship maneuverability. Comparisons have been made to the simulated data and measured data. The results show that the ship maneuvering model determined by our approach can seasonably reflect the actual motion of ship, and the parameter estimation procedure and algorithms are effective.
Rocznik
Tom
Strony
105--110
Opis fizyczny
Bibliogr. 11 poz., rys., tab.
Bibliografia
- 1 Beides, H.M., G.T. Heydt, 1991, Dynamic State Estimation of Power System Harmonics Using Kalman Filter Methodolo-gy, IEEE Transactions on Power Delivery, 6(4): 1663~1670
- 2 Farina A. et al, 2002, Tracking A Ballistic Target: Comparison of Several Nonlinear Filters. IEEE Transactions on Aero-space and Electronic Systems, 38(3): 854~867.
- 3 Kalman, R.E., 1960, A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME-Journal of Basic Engineering, 82 (D): 35~45
- 4 Lacy, S.L., et al, 2005, System Identification of Space Struc-tures. 2005 American Control Conference, 4:2335~2340
- 5 Leondes, C.T. et al, 1970, Nonlinear Smoothing Theory. IEEE Transactions on Systems Science And Cybernetics, 6(1): 63~71
- 6 Li, D., 1999, Ship motion and modeling, Harbin University Publication, Harbin.
- 7 Liu, J., et al, 2002, Application of ML to System Identification for Underwater Vehicle, Journal of Marine Science and Application, 11(1): 21~25
- 8 Narendra, K.S., K.Parthasarathy,1990, Identification and Con-trol of Dynamical Systems Using Neural Networks.IEEE Transactions on Neural Networks, 1(1):4~27
- 9 Nyarko, E.K., R.Scitovski, 2004, Solving the Parameter Identi-fication Problem of Mathematical Models Using Genetic Algorithms. Applied Mathematics and Computa-tion,153(3):651~658
- 10 Shi, C., et al, 2006, Collaboration to Enhance Development and Application of Shiphandling Simulators, in 12th IAIN World Congress / 2006 Internatioan symposium on GPS/GNSS. Jeju, Korea: 459-464.
- 11 Shi, H, et al, 2005. Improved System Identification Approach Using Wavelet Networks. Journal of Shanghai University (English Edition), 9(2): 159~163
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Bibliografia
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