电子测量与仪器学报

刘树聃.基于多重渐消因子强跟踪非线性滤波的故障参数联合估计[J].电子测量与仪器学报,2019,33(1):164-170

基于多重渐消因子强跟踪非线性滤波的故障参数联合估计

Fault parameter joint estimation based on multiple fading factors strong tracking nonlinear filter

DOI：

英文关键词:fault parameter joint filtering strong tracking filter seventh degree cubature Kalman filter

基金项目:河南省教改重点项目（［2015］061号）资助

作者	单位
刘树聃	1.许昌市耕新信息科学研究院，2.许昌职业技术学院航空工程学院

Author	Institution
Liu Shudan	1.Xuchang Gengxin Information Science Research Institute,2.Aviation Engineering Institute，Xuchang Vocational Technical College

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中文摘要:

为改进故障参数估计的精度和鲁棒性，提出基于多重渐消因子强跟踪七阶容积卡尔曼滤波（MST7thCKF）的故障参数联合估计算法。算法将故障参数扩展至状态向量，实现状态和故障参数联合滤波。然后，将多重渐消因子强跟踪滤波（MSTF）引入七阶容积卡尔曼滤波（7thCKF）的框架中，改进7thCKF在故障参数变化函数未知或者发生突变时的鲁棒性，提高故障参数的估计精度。仿真结果表明，相比MSTF均方根容积卡尔曼滤波（MSTSCKF）和7thCKF，所提算法具有更好估计精度。

英文摘要:

To improve the estimating precision and robustness of fault parameters, fault parameter joint estimation algorithm based on multiple fading factors strong tracking seventh degree cubature Kalman filter (MST7thCKF) is proposed. The algorithmextends the fault parameter to state vector, and realizes joint filtering of state and fault parameters. Then, the algorithm introduces multiple fading factors strong tracking filter (MSTF) into the frame of seventh degree cubature Kalman filter (7thCKF) to improve the robustness of 7thCKF when the fault parameters changing function is unknown or abruptly changed, and enhances estimating precision of fault parameters. Simulation results show that the proposed algorithm has better estimating precision than MSTF square rootcubature Kalman filter (MSTSCKF) and 7thCKF.

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