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| Abstract: |
| This paper proposes an adaptive unscented Kalman filter algorithm (ARUKF) to implement fault estimation for the dynamics of high-speed train (HST) with measurement uncertainty and time-varying noise with unknown statistics. Firstly, regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST, an augmented system is established, and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system. Then, considering the measurement uncertainties, a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy. Further, considering the unknown time-varying noise of the dynamics of HST, an adaptive UKF algorithm based on moving window is proposed to estimate the time-varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented. Finally, a five-car model of HST is given to show the effectiveness of this method. |
| Key words: high speed train Kalman filter adaptive algorithm robust algorithm unknown noise measurement uncertainty |
| DOI：10.11916/j.issn.1005-9113.21043 |
| Clc Number:TP13 |
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| Descriptions in Chinese: |
| 基于鲁棒UKF算法的高速列车动力学自适应故障估计 李科信,梁天添 (大连交通大学 自动化与电气工程学院，大连116028) 摘要：本文提出一种自适应无迹卡尔曼滤波器算法(ARUKF)来实现高速列车(HST)动力学系统的故障估计，该动力学系统具有测量不确定性和统计未知的时变噪声。首先，将执行器和传感器故障作为HST动力学系统的状态变量，建立增广系统，并将HST动力学系统的故障估计问题转变为增广系统的状态估计。然后，考虑测量不确定性，提出一个鲁棒下界来修改UKF的更新，以减少测量不确定性对滤波精度的影响。进一步考虑到HST动力学的未知时变噪声，提出一种基于移动窗口的自适应UKF算法来估计时变噪声，从而实现对HST动力学系统并发执行器和传感器故障的准确估计。最后，给出一个五辆车的高速列车模型来证明该方法的有效性。 关键词: 高速列车；卡尔曼滤波；自适应算法；鲁棒算法；未知噪声；测量不确定性 |
