基于改进强跟踪滤波器的发动机自适应模型

doi:10.16182/j.issn1004731x.joss.201808013

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (8): 2918-2927.doi: 10.16182/j.issn1004731x.joss.201808013

基于改进强跟踪滤波器的发动机自适应模型

杨曦中, 艾剑良

复旦大学航空航天系,上海 200433

收稿日期:2016-11-02 出版日期:2018-08-10 发布日期:2019-01-08
作者简介:杨曦中(1990-), 男, 河南焦作, 博士, 研究方向为飞行力学与飞行控制, 自适应滤波算法等; 艾剑良(1965-), 男, 江西临川, 博士, 教授, 博导, 研究方向为飞行器总体设计, 飞行控制与仿真技术等。

Design of Aircraft Engine Adaptive Model Based on Improved Strong Tracking Filter

Yang Xizhong, Ai Jianliang

Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China

Received:2016-11-02 Online:2018-08-10 Published:2019-01-08

摘要/Abstract

摘要： 针对发动机机载非线性实时性能模型,着重考虑实际工程应用,研究了真实工况下性能蜕化导致的基准模型与真实发动机的工作状态参数不匹配的问题。基于UKF (Unscented Kalman Filter)方法以及自适应强跟踪UKF方法,由机载传感器可测参数估计出传感器不可测的健康参数等信息,对Simulink中发动机基准模型的工作状态参数进行修正,建立了发动机自适应模型。在此基础上,提出一种改进的强跟踪UKF算法,经过仿真验证,该方法在满足实时性与鲁棒性的前提下,使得发动机自适应模型在动态过程中的精度显著提高。

关键词: 航空发动机, 自适应模型, UKF, 强跟踪滤波器, 状态估计

Abstract: Due to deterioration under real working condition, the aircraft engine performance parameters of benchmark model don’t match the real engine. This problem was studied based on in-flight engine nonlinear real-time performance model considering the practical engineering application. Immeasurable health parameters were estimated from measurable sensor parameters by UKF (Unscented Kalman Filter) and STUKF (Strong Tracking UKF). The engine adaptive model was established through modifying the benchmark model’s performance parameters in Simulink. A method of improved STUKF was proposed. Simulation results showed that the precision of engine adaptive model was greatly increased which satisfied real-time and robustness requirements in dynamic process.

Key words: aircraft engine, adaptive model, UKF (Unscented Kalman Filter), strong tracking filter, performance tracking

中图分类号:

TP391.9

杨曦中, 艾剑良. 基于改进强跟踪滤波器的发动机自适应模型[J]. 系统仿真学报, 2018, 30(8): 2918-2927.

Yang Xizhong, Ai Jianliang. Design of Aircraft Engine Adaptive Model Based on Improved Strong Tracking Filter[J]. Journal of System Simulation, 2018, 30(8): 2918-2927.

参考文献

[1] 上海航空测控技术研究所. 航空故障诊断与健康管理技术[M]. 北京: 航空工业出版社, 2013.
Shanghai Aero Measurement-Controlling Research Institute. Aviation Fault Diagnosis and Health Management Technology[M]. Beijing: Aviation Industry Press, 2013.
[2] Luppold R, Roman J, Gallops G, et al.Estimating In-flight Engine Performance Variations Using Kalman Filter Concepts[C]// Joint Propulsion Conference, AIAA, 1989: 2584.
[3] Simon D L, Litt J S.Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters[C]// ASME Turbo Expo 2005: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2005: 617-628.
[4] Dan S.A Comparison of Filtering Approaches for Aircraft Engine Health Estimation[J]. Aerospace Science & Technology (S1270-9638), 2008, 12(4): 276-284.
[5] 张海波, 陈霆昊, 孙健国, 等. 一种新的航空发动机自适应模型设计与仿真[J]. 推进技术, 2011, 32(4): 557-563.
Zhang H B, Chen T H, Sun J G, et al.Design and simulation of a new novel engine adaptive model[J]. Journal of Propulsion Technology, 2011, 32(4): 557-563.
[6] 潘鹏飞, 李秋红, 任冰涛, 等. 基于遗传算法的航空发动机部件特性修正[J]. 北京航空航天大学学报, 2014, 40(5): 690-694.
Pan P F, Li Q H, Ren B T, et al.Component map correction of aero-engine based on genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(5): 690-694.
[7] Lu F, Wang Y, Huang J, et al.Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter[J]. Energies (S1996-1073), 2015, 8(12): 13911-13927.
[8] 尉询楷, 杨立, 刘芳, 等. 航空发动机预测与健康管理[M]. 北京:国防工业出版社, 2014.
Wei X K, Yang L, Liu F, et al.Aeroengine Prognostics and Health Management[M]. Beijing: National Defense Industry Press, 2014.
[9] 朱柳. 基于滤波方法的航空发动机故障诊断研究[D]. 北京:清华大学, 2014:33-34.
Zhu L.Research of Aero-Engine Fault Diagnosis Based on Filtering Methods[D]. Beijing: Tsinghua University, 2014: 33-34.
[10] Chowdhary G, Jategaonkar R.Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter[J]. Aerospace Science & Technology(S1270-9638), 2010, 14(2): 106-117.
[11] Soken H E, Hajiyev C.Pico Satellite Attitude Estimation via Robust Unscented Kalman Filter in the Presence of Measurement Faults[J]. Isa Transactions (S0019-0578), 2010, 49(3): 249-256.
[12] 杨倩, 王洋, 赵红梅, 等. 基于强跟踪AUKF的目标跟踪算法[J]. 现代电子技术, 2016(17): 30-34.
Yang Q, Wang Y, Zhao H M, et al.Target tracking algorithm of AUKF based on strong tracking[J]. Modern Electronics Technique, 2016(17): 30-34.

基于改进强跟踪滤波器的发动机自适应模型

Design of Aircraft Engine Adaptive Model Based on Improved Strong Tracking Filter

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