结构的模糊可靠性分析与应用

简述了模糊可靠性理论、有关定义并探讨了理论在结构强度设计中的应用方法，最后以航天器复合材料舱段结构为例，把蒙特卡罗法和模糊可靠性理论结合起来，进行模糊随机可靠性分析和计算。     

导弹模糊增益调参鲁棒飞控系统设计

由于导弹飞控系统的高度非线性、时变性和不确定性,如何设计导弹高性能鲁棒控制系统是一项关键技术。利用模糊逻辑很强的知识综合和推理等特点,基于模糊控制和H∞鲁棒控制方法,建立了一种模糊参数化的增益调参飞控系统鲁棒设计方法,并利用线性矩阵不等式(LMI)技术,将鲁棒增益调参控制器设计转化成了相应的LMI凸优化问题,解决了复杂鲁棒控制器的求解问题。并将该方法应用于导弹鲁棒控制系统设计,仿真结果表明了该设计方法有效性。模糊逻辑、鲁棒控制以及线性矩阵不等式技术的有机结合可以设计具有全局稳定性及鲁棒性能要求的高性能导弹飞控系统。     

固体推进剂制造工艺危险性评定的模糊数学方法

根据国内固体推进剂研制的情况,借助于模糊数学的基本原理,提出了一种其工艺危险性评价的模糊数学方法,用隶属函数的概念描述了危险等级界限的模糊性;采用优化方法确定了影响因素的权重因子集合,并详细地对评判步骤、模糊关系矩阵的确立等问题进行了讨论。最后,通过对模糊数学方法与判分法和逻辑法的比较,讨论了新方法的特点。     

基于模糊神经网络的涡结构识别方法研究

研究气动光学传输效应产生的机理是红外成像末制导的共性基础技术之一,基于涡结构对光学传输效应进行建模是一种非常有效的方法,而涡结构的识别是其必要前提。文中提出一种新的涡结构识别方法,把折射率场经小波变换后的系数矩阵等效为具有一定纹理结构的图像,计算图像的共生矩阵及其统计量,由于涡结构模式复杂,特征量较多,设计了等价结构的模糊神经网络进行涡结构识别。与小波分解后直接提取特征量的识别方法相比,本文的方法从空、频角度更加准确全面地表征湍流涡结构模式,计算机仿真结果表明该方法优于神经网络的识别效率。     

竞争学习模糊神经网络及在导弹故障诊断中的应用

尽管基于解析冗余的故障诊断方法有许多突出的优点而越来越多地得到的研究和应用，但它依赖于系统的模型，当系统存在非线性或不确定性时，存在难以建模的困难，模糊神经网络可以通过学习建立系统的模型，且模型参数有明确的物理意义，初始参数易于选择，成为解决这一问题的优选途径，作者通过把模糊神经网络的学习转化为竞争聚类和线性优化问题，基于竞争聚类和最小二乘原理，提出了一种模糊神经网络学习算法，并在某伺服机构上进行     

MILDDC算法及其在飞机模块化生产网络分析中的应用

在中心度、节点最近邻和局部社团链接度最小增量有效融合的基础上,提出了一种基于中心度的最小链接度增量社团结构探测算法(Minimal Increment of Link Degree Based on Degree Centrality, MILDDC),并将MILDDC算法应用于某型飞机模块化生产网络并进行社团结构分析,计算结果说明,该算法具有一定的适用性,时间复杂度约为线性时间,并且对包含噪声、孤立点以及异形状的网络社团结构探测具有较强的鲁棒性。     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.     

Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

This paper describes the potentials of an aircraft model without and with winglet attached with NACA wing No. 65-3-218. Based on the longitudinal aerodynamic characteristics analyzing for the aircraft model tested in low subsonic wind tunnel, the lift coefficient (CL) and drag coefficient (CD) were investigated respectively. Wind tunnel test results were obtained for CL and CD versus the angle of attack α for three Reynolds numbers Re (1.7×¹⁰⁵, 2.1×¹⁰⁵, and 2.5×¹⁰⁵) and three configurations (configuration 1: without winglet, configuration 2: winglet at 0° and configuration 3: winglet at 60°). Compared with conventional technique, fuzzy logic technique is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between lift coefficients and drag coefficients with free-stream velocities and angle of attacks, and to illustrate how fuzzy expert system (FES) might play an important role in prediction of aerodynamic characteristics of an aircraft model with the addition of winglet. In this paper, an FES model was developed to predict the lift and drag coefficients of the aircraft model with winglet at 60°. The mean relative error of measured and predicted values (from FES model) were 6.52% for lift coefficient and 4.74% for drag coefficient. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%). The goodness of fit of prediction (from FES model) values were found as 0.94 for lift coefficient and 0.98 for drag coefficient which were close to 1.0 as expected.     

基于聚类模糊系统的动态数据野值剔除方法

针对影响遥测参数处理和分析的野值问题,提出了基于聚类法实时设计模糊系统实现动态数据野值辨识和剔除的新方法。该方法能够自适应跟踪不同变化特性的遥测参数,基于聚类法实现模糊系统的动态建模并获得预测值与观测值的残差序列,再按照狄克松准则实现野值的快速剔除。对实测数据的仿真实验表明：该方法能够显著降低动态建模的复杂度,快速跟踪信号变化,方法可行且有效。     

飞控系统故障检测的参数模糊自适应调节

提出通过模型参数的模糊比例积分自适应调节，以校正基于模型的飞控系统故障检测中的系统数学模型偏差。针对模型中误差差较大的迎角导数值，将俯仰角加速度的测量值与教学模型估计值之差作为比例积分调节的反馈量，动态实时校正数学模型参数。