Identification of reduced-order model for an aeroelastic system from flutter test data

Recently, flutter active control using linear parameter varying (LPV) framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by syn-thesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI) models by using sys-tem identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF) algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering tech-nique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML) estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.     

基于改进粒子群算法辨识Volterra级数的目标机动轨迹预测

目标机动轨迹预测是空战态势感知和目标威胁评估的重要前提。针对传统目标机动轨迹预测模型复杂度大、预测精度低等问题，结合目标机动轨迹时间序列的混沌特性，提出一种基于相空间重构理论和Volterra泛函级数的目标机动轨迹预测模型。该模型首先采用0-1检测法验证了目标机动轨迹时间序列具有混沌特性；其次，利用C-C法确定嵌入维数和时间延迟，对目标机动轨迹时间序列进行了相空间重构；然后，引入Volterra泛函级数预测模型，为了克服高阶Volterra核函数求解复杂的难题，提出一种混沌变异自适应粒子群算法，构建一种基于改进粒子群算法辨识的Volterra级数预测模型，并将其应用于目标机动轨迹预测；最后，将所提算法与卡尔曼滤波算法以及机器学习算法进行单步和多步预测对比，同时将改进粒子群算法与其他智能算法进行性能比较。仿真结果表明：所提预测模型具有良好的单步和多步预测性能，改进的粒子群算法具有参数辨识精度高、收敛速度快的优点。     

考虑星敏感器安装误差的弹道导弹捷联星光/惯性复合制导

弹道导弹捷联星光/惯性制导是在惯性制导基础上辅以星光修正的一种复合制导方法，能够显著提高导弹制导精度。由于星敏感器捷联安装在弹体上，安装误差会影响恒星测量的精度，进而影响复合制导精度，为此提出一种在线辨识并修正星敏感器安装误差的复合制导方法。建立了星敏感器观测量与数学平台失准角、星敏感器安装误差的关系方程，在导弹主动段关机后测量3颗独立的恒星获得6个观测量，利用最小二乘法估计出失准角和星敏感器安装、误差。改进了星光/惯性复合制导的最佳修正系数确定方程，直接修正了星敏感器安装误差的影响。数值仿真结果表明：所提方法可以有效地估计平台失准角和星敏感器安装误差，提高了捷联星光/惯性复合制导的精度。     

A robust star identification algorithm with star shortlisting

A star tracker provides the most accurate attitude solution in terms of arc seconds compared to the other existing attitude sensors. When no prior attitude information is available, it operates in “Lost-In-Space (LIS)” mode. Star pattern recognition, also known as star identification algorithm, forms the most crucial part of a star tracker in the LIS mode. Recognition reliability and speed are the two most important parameters of a star pattern recognition technique. In this paper, a novel star identification algorithm with star ID shortlisting is proposed. Firstly, the star IDs are shortlisted based on worst-case patch mismatch, and later stars are identified in the image by an initial match confirmed with a running sequential angular match technique. The proposed idea is tested on 16,200 simulated star images having magnitude uncertainty, noise stars, positional deviation, and varying size of the field of view. The proposed idea is also benchmarked with the state-of-the-art star pattern recognition techniques. Finally, the real-time performance of the proposed technique is tested on the 3104 real star images captured by a star tracker SST-20S currently mounted on a satellite. The proposed technique can achieve an identification accuracy of 98% and takes only 8.2?ms for identification on real images. Simulation and real-time results depict that the proposed technique is highly robust and achieves a high speed of identification suitable for actual space applications.     

基于Kriging形函数的线性时变结构模态参数辨识

近年来,对航空航天飞行器随时间变化的动力学特性研究需求越来越迫切。仅输出参数化时域的时变时间序列模型以其结构简约、精度高且跟踪能力强而成为研究热点,尤其是泛函向量时变自回归(FS-VTAR)模型已经得到了广泛应用。然而传统的FS-VTAR模型在保证其辨识优势的同时却需要针对不同时变结构选择合适的基函数形式及较高的基函数阶数,该过程相当复杂且耗时。本文借鉴无网格法中移动最小二乘(MLS)法构造形函数的思想,提出一种基于Kriging形函数的线性时变结构模态参数辨识方法。该方法首先引入自适应于辨识信号的Kriging形函数;再把时变系数在形函数上线性展开,利用最小二乘(LS)法得到形函数的展开系数;最后把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明:基于Kriging形函数的FS-VTAR模型相比于传统的FS-VTAR模型能有效地避免基函数形式的选择和较高的基函数阶数,且精度相当;相比于移动最小二乘法能有效地解决其数值条件问题且具有更高的模态参数辨识精度。     

Intelligent modeling and identification of aircraft nonlinear flight

In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.     

水平风洞模型自由飞试验技术研究现状及展望

介绍国外水平风洞模型自由飞试验技术研究现状,阐述水平风洞模型自由飞试验平台的组成、作用与意义,重点展望该试验技术的应用前景。对试验平台中动力相似模型设计加工技术、动力模拟技术、舵机运动控制技术、模型姿态实时精确测量技术、飞行控制系统设计与集成技术等关键技术问题进行分析,对发展该试验技术具有指导作用。完善水平风洞模型自由飞试验技术,把传统风洞试验拓展到流动-飞行-控制一体化试验,有利于全面研究和充分挖掘飞行器的气动性能与控制性能,对新一代飞机器的发展、新概念新技术的工程应用将起到重要的推动作用。     

复合材料飞机结构健康监测系统的若干问题探讨

针对飞机复合材料结构状态监测技术,从结构健康监测系统、结构健康监测研究对象和损伤识别方法,以及复合材料飞机的结构设计和结构健康监测系统三方面之间的关系进行阐述。分析了复合材料飞机结构健康监测系统的发展趋势,并讨论了制约该技术发展的因素。     

飞行阶段预防性维修风险识别和控制方法

运用头脑风暴法和层次分析法,对飞行四个阶段中航空维修出现的预防性维修风险进行识别和控制,并以机械日机务准备活动为案例进行分析,经实践验证,此方法可消除或降低飞行阶段航空维修过程中出现的预防性维修风险发生的概率,为其它机型开展此项工作奠定基础。     

FERA in parameter identification with application in low speed wind tunnel test

This paper presents a new approach which consists of a fuzzified eigensystem realization algorithm (FERA) to identify the parameters of the designed mini-UAV model performance in the low speed wind tunnel (LSWT) power on testing system. On the basis of the identification scheme, it is able to reduce system operational cost of wind tunnel tests and provide a tool for predicting results without resorting to on-site experiments. A variety of variables in model types and testing environment such as model profile, angle of attack, elevator, tunnel wind speed and power system (motor and propeller) are considered. The method based on logic devices is simple yet effective. The results obtained are compared to those obtained by the conventional wind-tunnel testing method. To verify effectiveness of the proposed methodology, both of simulations and real-world experiments are conducted. The results show that the working performance of the proposed method correlates well with expectation.