一种面向空天飞机再入的智能自适应复合控制方法

针对空天飞机再入横、侧向通道的姿态控制问题，设计了一种智能神经网络自适应复合控制方法，基于误差反馈学习准则在线更新神经网络权重以补偿全量姿态控制律输出的姿态控制指令。同时，面向再入过程横侧通道的强耦合问题，引入了耦合控制系数，以降低横、侧通道间的控制干扰。此外，提出了一种自适应链式控制分配律，在控制信号中引入正交优化多正弦激励，基于递推最小二乘方法对气动参数进行在线辨识，进而实时更新链式分配策略。最后，对空天飞机再入横侧向通道的神经网络自适应复合控制方法进行数学仿真校验，验证了该方法的有效性和鲁棒性。     

基于视惯融合的大型空间碎片质心位置辨识

大型自由翻滚碎片的质心是在轨操作基坐标系下的不动点，也是碎片连体基下动力学参数向卫星坐标系转换的基准，对其精确识别是提高碎片动力学参数辨识精度的关键。提出基于惯性单元测量数据与双目视觉定位数据融合的大型空间碎片质心位置识别方法。基于无力矩欧拉方程，获取附着到空间碎片表面的惯性单元间转换关系，利用该转换关系对惯性单元冗余测量数据优化，再优化求解惯性单元到质心点距离；利用双目视觉获取惯性单元上标记点动态坐标，再利用惯性单元到质心点距离，基于三点定位原理识别大型空间碎片的质心位置。以加入高斯白噪声的惯性单元与双目视觉测量数据进行仿真，结果表明优化解算后惯性单元实时测量数据的误差降低到1%以下，解算的质心位置三轴误差小于0.47mm；开展了地面试验，结果表明，解算的质心位置三轴误差小于0.49mm。仿真和试验证明，该方法能够为大型空间碎片的消旋、捕获任务提供准确的数据基准。     

大涵道比涡扇发动机低压转子现场动平衡技术

针对大涵道比涡扇发动机,建立基于最小二乘影响系数的低压转子现场动平衡方法,研制动平衡测试系统,搭建模型转子实验器,开展相关动力学特性计算与动平衡实验,并在真实发动机台架实验中验证其正确性、有效性与可靠性.结果表明:采用跟踪高脉冲、定比例方波的方法识别高齿信号,并对振动信号进行整周期截取、插值处理与多次平均,能够保证转速与相位测量的准确度与稳定性.1号支点对风扇不平衡较为敏感,5号支点对涡轮不平衡较为敏感,而2号支点对两者均敏感,但对风扇不平衡的敏感度更大.在保证动平衡效果的前提下,与三圆平衡法相比,低压转子现场动平衡技术能够提高操作便捷性,减少起停次数,降低时耗,具有一定的工程应用前景.      

交流伺服系统控制器在线参数自整定方法

伺服系统转动惯量的变化会影响伺服控制性能。为提高伺服性能需要对转动惯量进行在线辨识,实现伺服控制器参数的在线自整定。详细介绍了基于模型参考自适应的转动惯量在线辨识方法,提出了动态调整自适应增益和滤波器时间的方法,有效解决了自适应算法辨识速度和辨识精度的矛盾。根据转动惯量的辨识结果,利用对称优化法则,实时调整伺服控制器参数,以保证控制器动态性能的一致性和鲁棒性。仿真和试验验证了方法的有效性。     

空天飞行器星敏感器安装误差在线标定方法

空天飞行器高动态、长航时的运动特性可能导致一体化安装的惯性/天文组合导航系统中星敏感器与惯导间产生安装误差角。设计了一种星敏感器安装误差角动态辨识方法,建立了星敏感器安装误差角模型,设计了基于天文角度观测的星敏感器安装误差角动态辨识方案,分析了不同机动飞行方式下星敏感器安装误差角的可观测度。仿真结果表明,所设计的基于卡尔曼滤波的动态辨识方法能够在飞行器机动过程中快速地对星敏感器安装误差角进行在线标定,对安装误差角的标定值可以达到实际误差值的85%以上,有效地提高了组合导航系统的精度。     

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。