Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.     

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M...     

A model-free method for attitude estimation and inertial parameter identification of a noncooperative target

Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes.     

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.     

基于智能差分算法的摩擦力补偿方法研究

针对光电平台低速转动时，受摩擦力影响较大，使得速度跟随曲线出现“死区”现象，导致跟踪性能明显下降这一问题，提出了一种基于智能差分进化算法和Lurge摩擦模型的摩擦力补偿控制方法。通过采集记录光电转台正、反向匀速运动时的摩擦力大小，建立转台不同速度和摩擦力之间的对应关系。通过最小二乘法对摩擦模型静态参数进行分段拟合，采用智能差分进化算法辨识摩擦模型动态参数，并基于反馈的速度信息和获得的摩擦模型等效为摩擦补偿力矩输入到电流环控制输入端，实现平台平稳低速运行。实验结果表明：摩擦力补偿后速度响应误差由补偿前的±0.1°/s减小到±0.04 °/s，提出方法效果显著。     

基于数字多波束星载多通道VDES设计

阐述了甚高频数据交换系统(VDES)系统产生背景,针对目前普遍存在的系统通信速率受限以及自动识别系统(AIS)时隙冲突问题,提出一种全双工射频通信频段设计方法和时隙冲突解决途径。通过采用多个射频通道以及多个波束合成,利用阵列天线和数字波束合成(DBF)技术,将卫星大覆盖范围划分为多个相互独立区域,缩小单个天线波束视场覆盖范围,减少单波束范围内船舶数量,能有效降低AIS信号时隙冲突。以600 km卫星轨道为例,介绍了8通道及8个波束DBF设计方法,对波束视场和天线增益等进行了仿真计算。结果表明:多波束可覆盖±42°视场角范围,单波束视场角最大为±14°。