基于SR-UKF的潜艇水动力系数辨识方法

A SR-UKF-based method to identify submarine hydrodynamic coefficients

  • 摘要:
      目的  针对潜艇运动模型中水动力系数难以准确获取的问题,采用平方根无迹卡尔曼滤波(SR-UKF)算法进行系统辨识。
      方法  首先,以潜艇垂直面运动非线性数学模型为基础,结合SR-UKF算法,建立潜艇垂直面水动力系数辨识模型;然后,利用自动操舵控制潜艇在垂直面进行类正弦机动,将运动仿真生成的数据作为SR-UKF参数辨识的输入,并加入测量误差的影响;最后,通过数值仿真计算对潜艇垂直面机动的6个黏性无因次水动力系数进行辨识。
      结果  仿真结果表明,全部待识别水动力系数在3 000 s内均收敛至固定值,通过合适的初值选取,辨识结果与水动力试验所测定标准值的最大误差仅1.5%。
      结论  SR-UKF能有效应用于潜艇水动力系数辨识,并可进一步拓展用于实艇的水动力系数辨识。

     

    Abstract:
      Objectives  The square root unscented Kalman filter (SR-UKF) algorithm was developed for the identification of hydrodynamic coefficients, which are difficult to obtain accurately in submarine motion models.
      Methods  Firstly, the hydrodynamic coefficients identification model was established based on the nonlinear mathematical model of submarine motion in the vertical plane, combined with the SR-UKF algorithm. Then, a sinusoidal maneuvering in the vertical plane was carried out by the automatic steering method and the generated data in addition to the measurement errors were chosen as the input for SR-UKF parameter identification. Finally, six viscous hydrodynamic coefficients in the vertical motion plane were identified through a numerical simulation.
      Results  The simulation results show that, all identified hydrodynamic coefficients converge to fixed values within 3 000 seconds, and through the selection of appropriate initial values, the maximum error between the identification results and the standard values measured by a hydrodynamic test is only 1.5%.
      Conlusions  SR-UKF can be effectively applied to identify submarine hydrodynamic coefficients, and can be further extended to real ship coefficients identification.

     

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