Non-intrusive reduced-order model for predicting transonic flow with varying geometries

A Non-Intrusive Reduced-Order Model (NIROM) based on Proper Orthogonal Decomposition (POD) has been proposed for predicting the flow fields of transonic airfoils with geometry parameters. To provide a better reduced-order subspace to approximate the real flow field, a domain decomposition method has been used to separate the hard-to-predict regions from the full field and POD has been adopted in the regions individually. An Artificial Neural Network (ANN) has replaced the Radial Basis Function (RBF) to interpolate the coefficients of the POD modes, aiming at improving the approximation accuracy of the NIROM for non-samples. When predicting the flow fields of transonic airfoils, the proposed NIROM has demonstrated a high performance.     

一种航天器星光折射连续导航方法

针对星光折射航天器自主导航应用中观测缺失导航折射星造成误差上升甚至导航发散的情况，提出一种适用于航天器星光折射导航空白段的新方法。阐述了星光折射导航机理，给出了导航星观测窗口，进而设计基于神经网络的导航算法，该方法充分利用已有信息，有效预测并修正航天器状态信息，使星光空白段前后导航误差变化平稳，发挥星光折射间接敏感地平精度高的特点，保证了航天器高精度定位，且不需要添加硬件设备，算法简洁、实用。最后，通过计算机仿真校验了该导航方法的有效性。     

Unmanned aerial vehicle swarm mission reliability modeling and evaluation method oriented to systematic and networked mission

With the development of Unmanned Aerial Vehicle (UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations, and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decision-making work and supports the development of UAV-swarm-related work.     

Network analysis of Chinese air transport delay propagation

The Chinese air transport system has witnessed an important evolution in the last dec-ade, with a strong increase in the number of flights operated and a consequent reduction of their punctuality. In this contribution, we propose modelling the process of delay propagation by using complex networks, in which nodes are associated to airports, and links between pairs of them are assigned when a delay propagation is detected. Delay time series are analysed through the well-known Granger Causality, which allows detecting if one time series is causing the dynamics observed in a second one. Results indicate that delays are mostly propagated from small and regio-nal airports, and through flights operated by turbo-prop aircraft. These insights can be used to design strategies for delay propagation dampening, as for instance by including small airports into the system's Collaborative Decision Making.     

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.     

基于神经网络的仿真转台控制系统

在转台存在偏载、摩擦等不确定负载干扰的情况下,用神经网络与PID(Proportional-Integral-Differential)控制相结合的方法,设计了适应负载变化的转台控制系统.分析了基于BP(Back Propagation)神经网络的自适应PID控制器的基本原理,建立了转台位置控制系统的数学模型,并对控制系统进行仿真分析和实验验证,通过与传统PID控制的对比实验与仿真表明:所设计系统由于有自学习能力,能动态调整PID参数,使系统表现出良好的抗干扰能力和跟踪性能,证明了所设计系统的有效性.该算法结构简单,PID初始参数调整方便,易于在转台实时控制系统中应用.     

分数阶Hopfield型神经网络的全局渐近稳定性

研究了分数阶Hopfield型神经网络的全局渐近稳定性,通过LMI方法得到了一种实现系统全局渐近稳定性的LMI形式条件,通过实例仿真验证了结论的正确性。     

贝叶斯网络在飞控系统可靠性评估中的应用

把一种基于贝叶斯网络的故障树分析法应用于飞控系统俯仰控制功能的可靠性评估中,突破传统故障树分析法的局限性——各部件独立、状态为二值,不但可以计算系统的可靠性指标,而且可以定量给出某个事件或某几个事件在系统可靠性中所占的地位,找出系统的薄弱环节。     

Neural network development for the forecasting of upper atmosphere parameter distributions

This paper presents a neural network modeling approach to forecast electron concentration distributions in the 150–600 km altitude range above Arecibo, Puerto Rico. The neural network was trained using incoherent scatter radar data collected at the Arecibo Observatory during the past two decades, as well as the Kp geomagnetic index provided by the National Space Science Data Center. The data set covered nearly two solar cycles, allowing the neural network to model daily, seasonal, and solar cycle variations of upper atmospheric parameter distributions. Two types of neural network architectures, feedforward and Elman recurrent, are used in this study. Topics discussed include the network design, training strategy, data analysis, as well as preliminary testing results of the networks on electron concentration distributions.