一种基于EM-KS算法的连续变速颤振边界预测方法  

A New Method of Flutter Boundary Prediction for Progressive Variable Speed Based on EM-KS Algorithm

作　　者：刘俊豪 郑华[1] 段世强 裴承鸣[1] LIU Junhao;ZHENG Hua;DUAN Shiqiang;PEI Chengming(School of Power and Energy, Northwestern Polytechnical University, Xi′an 710072, China;Shanghai Aircraft Design and Reseavch Institute, Shanghai 201210, China)

机构地区：[1]西北工业大学动力与能源学院,陕西西安710072 [2]上海飞机设计研究院,上海201210

出　　处：《西北工业大学学报》2019年第6期1231-1237,共7页Journal of Northwestern Polytechnical University

基　　金：中央高校基本科研业务费(31020190MS702);国家科技重大专项(2017-V-0011-0062)资助

摘　　要：连续变速颤振试验(FTPVS)是近年来积极探索的一种颤振试验方案。针对该类试验中信号非平稳的特点,创新性地将期望最大化方法迭代优化的思想用于改善连续变速颤振信号的建模精度,提出了一种基于该方法的卡尔曼滤波平滑(EM-KS)算法,有效提高了时变参数的辨识性能。进而结合颤振时域判据,给出了可递推实现的连续变速颤振试验的颤振边界预测方法。最后通过数值仿真和实测数据对所提方法的可靠性与工程适用性进行了验证,结果表明,基于EM-KS颤振边界预测方法不依赖于平稳随机过程的假设,精确度可以满足实际工程需要。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.

关 键 词：EM-KS算法 卡尔曼滤波平滑 TVAR 颤振边界预测 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]