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    Tytuł:
    Identification of Ship Maneuvering Model Using Extended Kalman Filters
    Autorzy:
    Shi, C.
    Zhao, D.
    Peng, J.
    Shen, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/116777.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Manoeuvring
    Ship Manoeuvering Model
    Kalman filter
    extended Kalman filter (EKF)
    Ship Manoeuvrability
    ship handling simulator
    Turning Circle Test
    Zig-Zag Test
    Opis:
    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.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2009, 3, 1; 105-110
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parameter identification of ship maneuvering models using recursive least square method based on support vector machines
    Autorzy:
    Zhu, M.
    Hahn, A.
    Wen, Y.
    Bolles, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116455.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    ship manoeuvering
    recursive least square method
    ship manoeuvering model
    ship maneuverability prediction
    Support Vector Machines (SVM)
    empirical mode decomposition (EMD)
    Computational Fluid Dynamics (CFD)
    Extended Kalman Filter (EKF)
    Opis:
    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.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 23-29
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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