Abstract
The flutter test with progression variable speed is actively explored in recent years. This paper proposes an improved Kalman smoothing filter (EM-KS) algorithm based on expectation maximization for the non-stationary characteristics of the signal in this type of experiment, which can effectively improve the estimation accuracy of time-varying parameter modeling. Combining with the flutter time domain criterion, a new method for flutter boundary prediction of flutter test with progression variable speed that can be recursively implemented is given. Finally, the reliability and engineering applicability of this method are validated by numerical simulation and measured data. The results show that the flutter boundary prediction method based on EM-KS does not depend on the assumption of stationary stochastic process, and the accuracy can meet the actual engineering needs.
Highlights
西北工业大学学报 Journal of Northwestern Polytechnical University https: / / doi.org / 10.1051 / jnwpu / 20193761231
The flutter test with progression variable speed is actively explored in recent years
This paper proposes an improved Kalman smoothing filter ( EM⁃KS) algorithm based on expectation maximization for the non⁃stationary characteristics of the signal in this type of experiment, which can effectively improve the estimation accuracy of time⁃varying parameter modeling
Summary
西北工业大学学报 Journal of Northwestern Polytechnical University https: / / doi.org / 10.1051 / jnwpu / 20193761231 关 键 词:EM⁃KS 算法;卡尔曼滤波平滑;TVAR;颤振边界预测 中图分类号:TP391.9 文献标志码:A 文章编号:1000⁃2758(2019)06⁃1231⁃07 上实 现, 为 此, Matsuzaki 提出了离散系统下基于 Jury 判据的时域稳定性判据[11] 。 为了检验 EM⁃KS 算法的估计精度,分别通过带 有遗忘因子的递推最小二乘法( 下标 FFRLS,遗忘 因子取 0.99) 和卡尔曼滤波平滑算法的估计结果作 为对比,见图 2。
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