涡轮泵长程寿命的模糊可靠性设计和评估

根据涡轮泵的主要失效模式,建立了涡轮泵模糊可靠性设计方法。即对各部件进行模糊可靠性分配,再对涡轮叶片静强度、转子工作转速、转子轴疲劳强度、端面密封及轴承进行模糊可靠性设计,从而获得涡轮泵整机的可靠度。此处基于涡轮泵寿命服从威布尔分布,用模糊可靠性理论推导出涡轮泵长程寿命的可靠性评估公式。实例验算表明,评估值与预估值一致,设计与评估方法可行。     

液体火箭发动机地面试车实时故障检测参数的选取

合理有效的参数选择是液体火箭发动机地面试车实时故障检测系统的一个核心而基础的研究问题.本文首先进行了检测系统采集参数的需求分析;之后将液体火箭发动机测量参数作为方案层,各测量参数对发动机故障的敏感性、参数稳定性和参数相关性等作为准则层,测量参数对实时故障检测的有效性作为目标层,建立了液体火箭发动机参数选择层次结构模型;最后利用模糊层次分析法确定了某型LRE地面试车的实时故障检测参数.通过历史试车数据对参数选择的效果分析表明:所确定的检测参数能够全面表征LRE的运行状态,具有较强的故障表征能力和故障敏感性.从而,为科学合理的选择发动机地面试车实时故障检测参数提供了根据,解决了一直以来依靠定性方法确定发动机检测参数的问题.     

Fuzzy energy-based dual contours model for automated coronal hole detection in SDO/AIA solar disk images

The presence of coronal holes in solar disk plays an important role in influencing the space weather and generation of the solar wind. As such there lies a requirement in proper study and prediction of coronal holes occurs in the solar disk. This, in turn, arises the necessity of detection of coronal holes present in the solar disk. In this work, a Hough transformed inspired fuzzy-energy simulated dual contours-based segmentation technique has been proposed for the detection and extraction of holes in solar disk. In the proposed method Hough transform has been induced to initialize the contour for the contour-based method of segmentation. In the algorithm, two contours (active and static) have been initiated and made to evolve based on the energy function by incorporating the gray-scale intensity. Here in the work one contour is made to deform its shape while the other contour is kept static for the coronal holes detection purpose. The experiment has been carried out on few benchmark datasets and the corresponding outcomes have been compared with the results of other existing algorithms. The comparison results highlight the performance of the proposed technique in detection of coronal holes in solar disk.     

考虑跟踪性能约束的近空间飞行器控制器设计

针对一类可变后掠角的近空间飞行器(NSV)指令跟踪问题，考虑其受到外界扰动及参数不确定的影响，同时考虑系统的跟踪性能约束及姿态角速度约束，提出了一种基于模糊系统的切换控制器设计方法，确保系统在扰动影响以及给定约束下能够对给定信号进行稳定跟踪。建立了包含未知扰动及不确定项的近空间飞行器非线性切换模型；通过设计模糊系统对系统所受的总干扰进行实时估计，并基于反步法进行了切换控制器设计，在控制器中对干扰进行补偿；通过公共Barrier Lyapunov方法对系统稳定性及动态性能进行了分析。数值仿真算例校验了所提出方法的有效性及优越性。     

Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this paper, a novel adaptive fuzzy neural network (FNN) control scheme is proposed for the trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of output constraints and input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed by using the barrier Lyapunov function (BLF) to prevent the position tracking errors from violating the predefined output constraints. Then, an adaptive FNN controller is designed by using two FNNs to compensate for the lumped uncertainties and input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive FNN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero, while ensuring the position tracking errors within the output constraints even in the presence of input nonlinearities. To the best of the authors’ knowledge, there are relatively few existing controllers can achieve such excellent control performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control scheme.     

An improved α-cut approach to transforming fuzzy membership function into basic belief assignment

In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory(DST) framework, transformations from the other type of uncertainty representation into the basic belief assignment are needed. a-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional a-cut approach caused by its normalization step are pointed out in this paper. An improved a-cut approach is proposed, which can counteract the drawbacks of the traditional a-cut approach and has good properties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved a-cut approach.     

基于Fuzzy ARTMAP的GPS/INS组合导航系统故障诊断

将基于双状态传播器的状态χ2检验法(SCST)结合Fuzzy ARTMAP神经网络应用于GPS/INS紧组合导航系统故障的诊断和参数识别。首先,采用双状态传播器的状态χ2检验法对组合导航系统进行故障检测,得到故障的特征模式,并给出了双状态传播器重置时间间隔选择的充分条件;然后,利用Fuzzy ARTMAP神经网络结合特定的飞行器机动对故障模式进行分类,给出了一种新的分类方法;对于飞行器按照不同轨迹进行飞行的情况,也可有效的识别故障源。最后将分类的结果送入另一个Fuzzy ARTMAP神经网络进行故障参数的估计。仿真结果表明,针对组合导航系统中陀螺、加速度计、GPS信号的一度故障,此方法能有效进行检测和隔离,并能准确估计出故障发生时间和故障幅值。     

航天器姿态容错控制技术研究现状与发展

更远、更复杂的人类空间探测任务要求航天器具有更高的可靠性,因此航天器的容错控制技术受到了广泛关注。对航天器姿态系统的容错控制技术发展现状进行了分析,总结了国内外近年来航天器姿态容错控制的成果,重点分析了利用自适应控制、滑模控制、模糊控制、神经网络控制等理论开展容错控制的进展,并分别阐述了采用不同技术途径发展容错控制的优缺点。最后,展望了航天器姿态容错控制技术未来的发展。     

基于模糊数学的用户满意度评价

针对航天产品和服务,文章简要阐述了应用模糊数学进行用户满意度评价的具体应用方法和步骤,给出了具有实际意义的评价结论。     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.