波音737NG飞机APU喘振故障探讨

依据实际排故经验,结合手册以及厂家资料等,总结了日常波音737NG飞机出现的APU喘振或疑似喘振故障的主要原因和处理方法,重点强调了通过APU BITE内的输入监控检查APU各项参数对排故所起的参考作用。     

一种跳跃式返回再入的预测—校正制导方法

再入地球大气是探月飞船返回的关键阶段,再入制导是返回再入中的难点问题.飞船跳跃式再入过程复杂,标准轨道制导方法难以满足任务要求,因此具有高精度和强鲁棒性的预测—校正制导方法成为解决问题的首选.以探月飞船跳跃式再入为背景,设计了数值预测—校正制导律,研究了基于嵌套式积分算法的航程快速预报方法和基于有界试位法的倾侧角剖面快速更新算法,提出了一种气动系数误差和大气密度误差的在线参数辨识方法,并基于最大偏差法和蒙特卡洛打靶法进行了仿真分析.结果表明,预测—校正再入制导方法在跳跃式再入问题上具有较高的精度和较好的鲁棒性.5 000 km再入航程时,开伞点误差在2.5 km以内.     

空间机械臂视觉测量技术研究

空间机械臂技术是维护空间站、执行指定任务、保障航天员出舱作业的安全等必不可少的关键技术。视觉测量技术则是保证空间机械臂顺利完成空间遥操作任务的前提。基于此前提,首先对空间机械臂视觉测量技术进行了概述,并分别对手眼关系标定、标志器识别以及相对三维位姿测量等关键技术进行了阐述;然后,以加拿大机械臂为例提出了一种基于边缘特征的标志器识别算法,给出了具体的识别流程,并采用一种基于非迭代的相对位姿测量算法实现了位姿求解;最后,针对标志器识别算法和位姿测量算法给出了基于仿真图像的实验结果和机械臂原理样机集成实验结果。最终实验结果表明:提出的空间机械臂视觉测量方法合理可行,满足预期的技术指标要求,具有较强的工程应用价值。     

基于双轴转位机构的激光捷联惯导系统在线标定方法研究

本文主要研究了在静基座条件下,利用双轴转位机构实现激光捷联惯导系统在线标定的两个问题：（1）推导了在线标定的一种误差参数模型,明确了模型成立的条件;（2）提出了设计多位置翻滚实验的位置编排原则。此方法具有实现简单、标定时间短、精度高的特点。     

综合通信导航识别系统的故障注入方法研究

针对综合通信导航识别系统的综合化、模块化特点，在综合分析系统故障检测诊断机制、构建系统故障信息库的基础上，设计了一种面向综合通信导航识别系统的软件故障注入方案，可提高综合通信导航识别系统故障诊断软件的效能。通过利用此方案进行故障注入试验，验证了综合通信导航识别系统故障诊断软件的功能和性能。     

基于模型拼接技术的四旋翼无人机全飞行包线建模

四旋翼无人机在军事和民用领域都有广泛的应用前景。然而，四旋翼无人机具有机载传感器准确性低与可靠性差、不同飞行条件下动力学模型差异大等特点。本文以四旋翼无人机为研究对象，系统地开展了飞行路径重构、频率域系统辨识、全飞行包线建模的相关研究工作。针对机载低成本传感器存在显著常值偏差与量测噪声统计学特性未知的缺点，采用基于EKF的飞行路径重构技术对在飞行试验中无人机的机体空速进行重构；针对不同飞行速度下四旋翼无人机动力学模型差异大的特点，采用频域法辨识得到不同飞行条件下的本体动力学模型；针对四旋翼无人机大跨域的高精度控制需求，利用模型拼接技术，系统性地提出了全飞行包线建模方案，经过验证，所提出模型拼接技术是准确可靠的。     

基于网格自适应的飞行器防热材料热传导系数辨识

为准确辨识高超声速飞行器防热材料热传导系数，构造了针对热传导偏微分方程系统的约束泛函极值问题，基于表征多项式逼近性能的Weierstrass定理，采用Lagrange多项式对热传导系数进行参数化建模，进而将无穷维的约束泛函极值问题转化为有限维的非线性规划问题，再利用基于动态优化方程的优化方法将此非线性规划问题转化为常微分方程初值问题进行求解。为从较有限的测量数据中准确地“学习”热传导系数，建立采用“过拟合”判别准则的网格自适应迭代算法改善辨识精度。研究表明本文采用的辨识策略与优化方法有效，辨识结果可以较准确地反映材料热传导系数的变化规律。     

A star identification algorithm based on radial and dynamic cyclic features of star pattern

A full-sky star identification algorithm based on radial and dynamic cyclic patterns is presented with the aim of solving the “lost-in-space” problem. The dynamic cyclic pattern match is applied with a maximum cumulate comparison method to identify sensor-catalog pairings in initial match, which substantially eliminates the effects of the star position noise, magnitude noise, and false stars. After initial match pairings of stars are obtained, a chain part extension technique is employed to quickly search for the longest match chain as the final result. Experimental results indicate that the proposed algorithm is highly robust to star position noise, magnitude noise and false stars. In a series of simulations, the identification rate of the algorithm is 97.50% with 2.0 pixels star position noise, 96.90% with 0.4 Mv star magnitude noise and 95.30% with four false stars respectively. Moreover, the algorithm achieves an identification rate of 58.08% when only six stars are in the field of view.     

A global solution for robust parameter design of aeronautical electrical apparatus based on interactions analysis and polynomial fitting

Robust Parameter Design (RPD) has been widely applied for improving quality and reliability of products. One of the key drawbacks of applying RPD using Taguchi method is that the stable factors may not be independent of the adjustment factors, resulting in unsatisfactory design. Moreover, the Taguchi method cannot guarantee global optimality since the levels set in the experiment are usually discrete to ensure orthogonal design. In this paper, robust solutions of the stable factors are obtained via a nonlinear model based on polynomial fitting; while the adjustment factors are obtained via interactions analysis so that they are independent of the stable factors. In particular, the values of the adjustment factors are determined by output offset compensation so as to achieve robustness of the design scheme. An example on the design of an aeronautical electrical apparatus is presented to illustrate the procedure. The results show that the proposed method can take full advantage of the nonlinearity in the response and achieve the desired outcome.     

Space object material identification method of hyperspectral imaging based on Tucker decomposition

Space object material identification method based on Tucker decomposition is proposed and demonstrated experimentally. Space target generally has a low spatial resolution because of the limitation in detection distance. Hyperspectral imaging (HSI) technology can capture the image from visible light to shortwave infrared continuously while providing the spatial and spectral information of the target, thereby introducing a new space target detection and identification approach. However, the data obtained by the HSI system often contain mixed pixels, thereby causing difficulties in target material identification. In this work, a material identification method of hyperspectral data based on Tucker decomposition is proposed by combining mixed spectral theory with tensor decomposition. The feasibility of the method is verified by using satellite model hyperspectral data with different spatial resolutions compared with the endmember obtained from the non-negative matrix decomposition (NMF), independent component analysis (ICA), tensor singular value decomposition (t-SVD). The average CORR of NMF, ICA, t-SVD and the proposed method is 0.2694, 0.5818, 0.6397 and 0.937, correspondingly. Therefore, the proposed method has demonstrated a more remarkable performance in terms of material identification, the analysis results of the material abundance distribution that used the proposed method.