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A model-free hull deformation measurement method with time delay compensation:

机译:具有时延补偿的无模型船体变形测量方法:

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摘要

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.
机译:对船体变形的估计和补偿是船舶建立统一空间姿态的必不可少的步骤。现有的船体变形测量方法依赖于预先建立的变形模型,而不正确的变形模型会降低变形估计的准确性。为解决这一问题,本文提出了一种无变形模型的船体变形估计方法,该方法利用神经网络拟合船体变形。为了在线训练神经网络,将神经网络的连接权重视为系统状态变量,可以通过Unscented Kalman滤波器进行估计。同时,考虑惯性数据的时滞问题,提出了一种基于四元数姿态矩阵的时延补偿方法。仿真结果表明,即使惯性数据是异步的,所提出的方法也不需要变形模型就能获得较高的估计精度。

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