高超声速飞行器动态神经网络反推自适应控制

针对高超声速飞行器纵向模型的高度非线性特点,在考虑模型不确定性的情况下,提出了高超声速飞行器动态神经网络调节函数反推自适应控制方法.对给定的速度指令,引入积分型Lyapunov函数设计跟踪控制器,取消了控制增益一阶导数上界的限制,且避免了控制器的奇异性;对给定的高度指令,引入调节函数技术,设计了反推控制器,避免了将模型化为严反馈形式;采用动态神经网络对未知系统动态进行自适应在线逼近.根据Lyapunov理论证明了设计的控制律保证了闭环系统的稳定性与指令跟踪的精确性.仿真结果验证了该方法的可行性及有效性.     

一种星-箭连接动态界面力的深度学习反演方法

针对于星-箭连接动态界面力无法通过力传感器直接测量,且典型时域动载反演方法难以准确计算界面力的时域变化等难点,提出了基于长短时记忆(LSTM)神经网络的星-箭界面力深度学习反演方法。首先通过卫星地面测试试验得到数据依据,以卫星主体结构的加速度测量数据为输入层,以星-箭界面力测量数据为输出层,利用LSTM神经网络建立输入和输出间的反演映射关系模型,实现卫星在发射过程中较高精度的界面力反演。进而,设计并开展了某典型卫星结构的正弦扫频和随机振动实验,测试LSTM界面力反演方法的可行性。结果分析可知,所提出的基于LSTM深度学习反演方法能够精确地获得动态界面力时程数据,两项性能指标均优于目前典型的载荷反演方法。     

Optimization of beamforming and path planning for UAV-assisted wireless relay networks

Recently, unmanned aerial vehicles （UAVs） acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point （AP） is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming （BF） and UAV path planning. First of all, we study a dual-hop amplify-and-forward （AF） wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station （BS）. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal~to-noise ratio （SNR） of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability （OP） expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。     

基于BP网络的机组工作量评估方法研究

为研究机组工作量的评估问题,搭建了模拟飞行实验平台,设定了7种不同负荷的飞行任务。模拟飞行中,对被试的生理指标进行测量,并采集其主观评价值。采用BP网络进行数学建模,将生理指标测量值作为输入、NASA-TLX表法的评价值作为输出,利用输入、输出模式对对网络进行训练,并对该方法进行验证。结果表明,基于BP网络的机组工作量评估方法,较传统方法更为稳定、精确,且大大降低了被试数量。提高训练模式对的数量、简化生理指标的测量过程,并保证测量精度是该方法成功的关键。     

有线电视HFC网络与光纤链路设计

本文论述了双向传输有线电视ＨＦＣ网络结构并比较了其优缺点，文中还就工程应用的实例讨论了光纤链路的设计问题。     

Design and Application of Discrete Sliding Mode Control with RBF Network-based Switching Law

This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to form the switching law in a direct and online manner with sliding-mode-related inputs and by approximating evaluation function; and the method to adjust its parameters is devised. Finally, comparable experiments are carried out to verify the application of the proposed approach to an inner-axis driven by a direct current (DC) torque motor through extra-low speed servo for a high precision flight simulator, and the axis works under deteriorating conditions such as periodically fluctuating torques of motor, nonlinear friction, and time-varying model parameters. The results show that the combined SMC can effectively improve the servo performance, for instance, to a stable 0.000 08 (°)/s speed response, the tracking error would be within 0.000 08° in 98% of operating times. Moreover, the hybrid nature of the approach imparts the RBF network the features of removing offline training and ease to set initial parameters.     

An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model

With the progress of high-bypass turbofan and the innovation of silencing nacelle in engine noise reduction, airframe noise has now become another important sound source besides the engine noise. Thus, reducing airframe noise makes a great contribution to the overall noise reduction of a civil aircraft. However, reducing airframe noise often leads to aerodynamic perfor-mance loss in the meantime. In this case, an approach based on artificial neural network is intro-duced. An established database serves as a basis and the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the most satisfactory configuration is selected for validating computations through the trained BP network. On the basis of the artificial neural network approach, an optimization pro-cess of slat cove filler (SCF) for high lift devices (HLD) on the Trap Wing is presented. Aerody-namic performance of both the baseline and optimized configurations is investigated through unsteady detached eddy simulations (DES), and a hybrid method, which combines unsteady DES method with acoustic analogy theory, is employed to validate the noise reduction effect. The numerical results indicate not merely a significant airframe noise reduction effect but also excel-lent aerodynamic performance retention simultaneously.     

用神经网络法编程加工轮毂上特殊曲面的探讨

根据轮毂上曲面的特殊性,提出了采用神经网络方法进行编程,实现数控加工,以取代传统的加工方法的设想。     

人工神经元网络在变压器分接头实时控制中的应用

本文提出了一种利用人工神经元网络实时控制变压器分接头的新方法。研究结果表明该方法具有快速准确等优点，适应电力系统高度自动化的要求，并为变压器分接头实时控制开辟了一条新途径。