卫星时滞系统的强跟踪鲁棒扩展Kalman滤波
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大连交通大学自动化与电气工程学院 大连 116028

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TN713

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辽宁省教育厅基金(JDL2020020)、常州市应用基础研究计划(CJ2020007)项目资助


Strong tracking robust extended Kalman filtering for delay systems of satellite
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School of Automation and Electrical Engineering, Dalian Jiaotong University,Dalian 116028,China

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    摘要:

    针对具有状态时滞和未知不确定性的卫星姿态控制系统,提出一种强跟踪鲁棒扩展Kalman滤波器,以实现执行器和传感器的并发故障估计。首先,考虑系统噪声,视故障为系统的辅助变量,建立增广时滞非线性系统。然后,提出鲁棒扩展Kalman滤波器,引入鲁棒上界以减少线性化误差。进一步,针对系统过程不确定性导致预测协方差精度较低的问题,引入基于多重次优渐消因子的强跟踪算法,以降低不确定性对滤波精度的影响。最后,给出仿真算例,将所提出方法与鲁棒扩展Kalman算法和扩展Kalman算法进行对比仿真。仿真结果表明,相较于其他两种算法,所提出方法的状态估计和故障估计均方根误差的平均值分别降低了69.2%、60.6%和88.1%、78.9%,仿真结果验证了设计方案的有效性。

    Abstract:

    A strong tracking robust extended Kalman filter is proposed for attitude control systems of satellite with state delay and unknown uncertainty to implement concurrent fault estimation of actuator and sensor. Firstly, considering the system noise, taking the faults as the auxiliary variables of the system, an augmented time-delay nonlinear system is established. Then, a robust extended Kalman filter is proposed, and a robust upper bound is introduced to decrease the linearization error. Further, for low accuracy of prediction covariance caused by system process uncertainties, a strong tracking algorithm based on multiple sub-optimal fading factors is introduced to decrease the influence of uncertainty on filtering accuracy. Finally, a simulation example is given to compare the proposed method with the robust extended Kalman algorithm as well as the extended Kalman algorithm. The simulation results show that, compared with the other two algorithms, the average value of the root mean square error of the state estimation and fault estimation by the proposed method is reduced by 69.2%, 60.6% and 88.1%, 78.9%, respectively. The simulation results verify the effectiveness of the design scheme.

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李科信,郑祥,梁天添.卫星时滞系统的强跟踪鲁棒扩展Kalman滤波[J].电子测量技术,2023,46(3):86-91

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  • 在线发布日期: 2024-02-26
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