先进战斗机过失速机动大气数据融合估计方法

杨朝旭; 郭毅; 雷廷万; 李荣冰

doi:10.7527/S1000-6893.2019.23456

航空学报 >

2020 , Vol. 41 >Issue 6: 523456 - 523456

DOI: https://doi.org/10.7527/S1000-6893.2019.23456

流体力学与飞行力学

先进战斗机过失速机动大气数据融合估计方法

杨朝旭 ,
郭毅 ,
雷廷万 ,
李荣冰

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1. 中国航空工业成都飞机设计研究所, 成都 610091;
2. 南京航空航天大学自动化学院, 南京 210016

收稿日期: 2019-09-10

修回日期: 2019-09-21

网络出版日期: 2019-10-24

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Air data fusion and estimation method for advanced aircrafts in post-stall maneuver

YANG Zhaoxu ,
GUO Yi ,
LEI Tingwan ,
LI Rongbing

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1. AVIC Chengdu Aircraft Design and Research Institute, Chengdu 610091, China;
2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received date: 2019-09-10

Revised date: 2019-09-21

Online published: 2019-10-24

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摘要

可控的过失速机动是先进战斗机超机动性能的重要标志，飞机飞行包线的扩大已超出传统的大气数据系统测量范围，可靠的迎角、侧滑角、总压、静压等飞行大气数据是制约先进战斗机过失速机动中飞行控制的关键因素。以中国推力矢量验证机为对象，基于过失速机动飞行试验的数据，开展大气参数估计与验证研究。结合过失速机动的时间与空间特性，研究了基于风速、地速、空速矢量和惯性姿态、导航参数的大气参数融合计算方法；针对过失速大迎角状态下飞机周围气流非定常、模型非线性导致的融合大气参数误差的复杂特性，进一步构建深度神经网络，对机动状态融合迎角、侧滑角的强非线性误差进行拟合。仿真和飞行试验表明：该方法可在大迎角飞行状态下实现主要大气参数的融合估计，过失速机动过程中融合迎角误差优于2.3°，融合得到的大气参数可为过失速大迎角机动飞行控制提供可靠的大气参数状态反馈。

关键词： 过失速机动; 大迎角; 大气参数; 风场估计; 惯性导航; 深度神经网络

本文引用格式

杨朝旭 , 郭毅 , 雷廷万 , 李荣冰 . 先进战斗机过失速机动大气数据融合估计方法[J]. 航空学报, 2020 , 41(6) : 523456 -523456 . DOI: 10.7527/S1000-6893.2019.23456

Abstract

Post-stall maneuver capability is an important feature of advanced aircrafts, and the extension of flight envelope exceeded the measurement range of traditional air data systems. air data including angle of attack, angle of sideslip, total and static pressure are the essential factors for the control of advanced fighters in post-stall maneuver. An air data fusion and estimation method was proposed and validated based on a thrust vector technology test. The first step is to calculate air data by blending vectors of wind speed, ground speed, and navigation parameters such as attitude and rotation rate. A kind of deep neural network with multiple hidden layers and better feature expression and ability to model complex mapping was designed, trained and employed to fit the calculated angle of attack errors with strong nonlinearity and hysteresis, which were due to the unsteady flow around the aircraft and nonlinear relationship among the flight state parameters. Simulation and flight data shown that the proposed method can complete the estimation of air data even at high angle of attack maneuver and achieve angle of attack parameter with an error no more than 2.3°. The estimated air data can be provided to the flight control system as reliable state feedback.

Key words： post-stall maneuver; high angle of atteck; air data; wind estimation; inertial navigation; deep neural network

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