Adaptive Unscented Kalman Filter Design for Variable Cycle Engine

被引：0

作者：

Xiao H.-L. ^{[1
]}

Peng K. ^{[2
]}

Wang Z.-S. ^{[3
]}

Fu J.-F. ^{[2
]}

Chen H. ^{[1
]}

Yan B. ^{[4
]}

机构：

[1] School of Energy and Electrical Engineering, Chang’an University, Xi’an

[2] School of Power and Energy, Northwestern Polytechnical University, Xi’an

[3] China Aviation Industry New Aviation Plain Aviation Equipment Co. Ltd., Xinxiang

[4] AVIC Beijing Avionics Engine Control System Technology Co. Ltd., Beijing

来源：

Tuijin Jishu/Journal of Propulsion Technology | 2023年 / 44卷 / 05期

关键词：

Adaptive unscented Kalman Filter; Kalman filter; Parameter estimation; Probability density function; Variable cycle engine;

D O I：

10.13675/j.cnki.tjjs.2208071

中图分类号：

学科分类号：

摘要：

An adaptive unscented Kalman filter is designed for variable cycle engine health parameter estimation. The algorithm establishes adaptive update equations for process noise covariance and measurement noise covariance by maximizing the posteriori density function. Unlike the traditional unscented Kalman filter design，where prior parameters need to be set according to experience，the designed adaptive unscented Kalman filter can reduce the impact of human factors on the filter performance. A simulation test of health parameter estimation was conducted for a variable cycle engine with CDFS，and the designed adaptive unscented Kalman filter algorithm was verified by simulation comparison. The results show that the average estimation error of health parameter was no more than 2% under single-parameter degradation condition，and no more than 1.8% under multi-parameter degradation condition. The performance of this algorithm is better than that of the augmented Kalman filter and the traditional odorless Kalman filter，and the performance is improved by 9.5% compared to the traditional unscented Kalman filter. © 2023 Journal of Propulsion Technology. All rights reserved.

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