基于反馈粒子滤波的船舶模型参数辨识

Parameter identification of ship model based on feedback particle filter

  • 摘要:
      目的   近年来,随着船舶朝着大型化、高速化、智能化的方向发展,船舶动力定位技术显得尤为重要。为了在动力定位系统中建立运动数学模型,需要确定模型中各参数的值。
      方法   首先,以一艘挖泥船为研究对象,建立船舶运动数学模型,并分离出纵荡运动模型以及横荡与艏摇运动模型;然后,基于系统辨识理论和反馈粒子滤波算法辨识模型中的未知参数,包括2个主推进器和1个侧推进器的推力系数;最后,进行仿真实验,求得待辨识的参数值。
      结果   通过与扩展卡尔曼算法的比较,显示反馈粒子滤波算法对参数辨识的效果更好,验证了反馈粒子滤波算法的可靠性。
      结论   该方法在船舶动力定位系统中具有良好的应用前景。

     

    Abstract:
      Objective   In recent years, ships are becoming larger, faster and more intelligent, so the ship dynamic positioning technology is particularly important. In order to establish a kinematic mathematical model in the dynamic positioning system, we need to determine the values of the parameters in the model.
      Methods   Firstly, a dredger is used as the research object to establish the mathematical model of the ship movement, and the surge motion model and the sway and yaw motion model are extracted. The unknown parameters in the model are identified based on the system identification theory and the Feedback Particle Filter(FPF)algorithm, including the thrust coefficients of two main thrusters and one side thruster. Then, the parameters to be identified are obtained by the simulation experiment.
      Results   Finally, by comparing with the Extended Kalman Filter(EKF)algorithm, it is shown that the FPF algorithm is better at identifying the parameters and the reliability of FPF algorithm is verified.
      Conclusion   This method has good application prospect in ship dynamic positioning system.

     

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