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基于AUPF算法的水下履带车动力学参数估计
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Estimation of Motion Parameters of a Underwater Track Mining Vehicle Based on Adaptive Unscented Particle Filter Algorithm
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    摘要:

    针对多金属结核采矿车在稀软底质行驶作业时有效驱动轮半径和履带打滑率等 动力学参数难确定的问题,基于多金属结核采矿车的牵引力分析和液压驱动系统的负载特性 分析,建立用于多金属结核采矿车动力学参数估计的高阶非线性系统模型 . 针对基于高斯模 型的无迹卡尔曼滤波(Unscented Kalman Filter,UKF)算法无法在非线性系统取得较高估计精 度的问题,提出利用基于蒙特卡洛采样原理的自适应无迹粒子滤波(Adaptive Unscented Par? ticle Filter,AUPF)算法进行动力学参数测算方案,通过自适应无迹卡尔曼滤波(Adaptive Un? scented Kalman Filter,AUKF)改善粒子滤波(Particle Filter,PF)的概率密度函数,解决 PF 容易 发散和UKF估计精度不高的问题. 实验结果表明,AUPF算法得到的多金属结核采矿车的动力 学参数误差均小于最大允许误差,满足精准在线测算的性能需求.

    Abstract:

    Aiming at the difficulty in determining the motion parameters such as effective drive wheels and track slip rate of a polymetallic nodule mining vehicle, based on the modeling of traction force and load characteristics of tracks, a higher-order nonlinear system for estimating the motion parameters of a polymetallic nodule mining vehicle is proposed. To address the problem that the UKF based on Gaussian model cannot achieve high estimation accuracy in high-order nonlinear systems, an adaptive traceless particle filtering algorithm (AUPF) based on Monte Carlo sam? pling principle is proposed,and the adaptive traceless Kalman filter (AUKF) is used to improve the Particle Filter (PF) by refining the probability density function, which solves the shortcomings that the PF is easy to diverge and the estimation accuracy of UKF is low. The experimental results show that the AUPF algorithm can achieve an accurate estimation of the motion parameters of polymetallic nodule mining vehicles, and there is an important engineering ap? plication value.

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陈昱衡 ,吴鸿云 ,边有钢 .基于AUPF算法的水下履带车动力学参数估计[J].湖南大学学报:自然科学版,2022,49(8):29~35

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  • 在线发布日期: 2022-09-07
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