柔性支撑控制力矩陀螺动力学建模与控制

针对柔性支撑引起控制力矩陀螺(CMG)框架控制性能下降的问题，建立了柔性支撑条件下控制力矩陀螺的动力学模型，分析了柔性支撑扰动力矩的耦合机理并给出了解析模型.据此，设计了一种积分滑模控制策略，用于提升对CMG框架动力学特性变化和新增扰动力矩的鲁棒性.仿真结果表明，该控制策略相比传统的PID控制具有更好的动态过程和稳态性能，提升了柔性支撑下控制力矩陀螺框架速度的闭环控制性能.     

基于DOA聚类算法的ARM抗有源诱偏技术研究

有源诱偏技术是雷达对抗反辐射导弹的一种重要手段,可以大大降低反辐射导弹的作战效能。为提高对敌目标的精确打击能力,在分析闪烁诱饵诱偏原理的基础上,针对有源诱偏干扰下被动雷达测角精度与稳定度不高的问题,通过对有源诱偏信号的时域特征进行分析,提出一种基于脉冲前沿检测的DOA聚类分选算法,找出前沿超前的辐射源信号,实现了高性能抗有源诱偏干扰和高精度抗干扰测向。仿真结果表明,该技术可以有效对抗四点源有源诱偏干扰。     

航天嵌入式软件数组越界缺陷特征研究

根据统计,数组越界是航天嵌入式软件开发过程中出现最多且最容易被遗漏的缺陷类型之一.目前自动化检测数组越界多基于抽象解释、符号执行、程序模型检验等方法,这些方法在误报、漏报、可扩展性等方面的表现依赖于软件及缺陷特征.分析了近三年航天嵌入式软件第三方测试中发现的94个数组越界问题,从缺陷模式和缺陷表现形式两方面分析得出10项航天嵌入式软件数组越界缺陷特征,并提出对设计具体检测方法关键的若干启示.进一步基于这些特征和启示探讨了数组越界检测算法针对中断驱动型程序的改进方向.     

一种基于OPTICS聚类的仅测角导航目标检测算法

仅测角自主导航方法具有设备简单、复杂度低，功耗低的优点，在空间任务中具有广泛的应用前景.针对中远距离下空间目标特征少的特点，提出了一种利用基于OPTICS聚类算法的空间目标检测方法，可用于仅测角导航过程中的目标检测.对原始星图进行预处理提取星点及目标点，并结合星图识别的结果选择部分帧，使用经过改进的OPTICS聚类方法获得目标运动轨迹.最后，使用本文中的算法对软件仿真出的含有目标的高精度星图进行处理验证了算法的可行性.在卫星相对于空间目标抵近过程中，目标检测的水平误差及垂直误差小于0.15°的帧数分别占到了85.4%以及99.6%.相比AVANTI实验中的目标检测方法，减少了在轨任务中相关参数的调节，进一步提升了算法的自主性.     

基于模态反转的半球谐振陀螺零位自校准方法

针对半球谐振陀螺的零位漂移问题，本文研究了基于模态反转电路控制技术的零位自校准方法。首先，从动力学模型引入阻尼不对称、频率裂解等非理想因素，讨论半球谐振陀螺的工作机理及驻波漂移特性。其次，通过理论分析模态反转的零位校准以及虚拟进动控制的模态反转技术。最后，基于FPGA数字控制平台，设计并实现了半球谐振陀螺零位自校准半实物仿真实验。实验结果表明，基于模态反转的半球谐振陀螺零位校准，实现了半球谐振陀螺的零位从15 降低至2 ，短期零偏稳定性和长期稳定性也分别提高了11.8％和45.86％，有效地抑制了零位漂移，提高了半球谐振陀螺测量精度。     

微纳聚合体卫星的对称式重构规划算法

为解决微纳聚合体卫星变构过程中，聚合体各部分之间的动力学耦合导致的卫星整体姿态翻转或紊乱，提出了一种对称式变构规划算法。首先建立了铰链约束下的漂浮基多刚体系统动力学模型，研究了重构过程中各运动模块对本体姿态的影响，提出当每两个运动模块位置与转动方向满足对称性条件时，两者对本体姿态的影响可在一定程度上相互抵消。基于上述理论，在A*算法中引入最优分配度量和对称性判定，设计了并行对称重构规划算法。仿真结果表明，该规划算法可实现重构过程中多模块并行、对称运动，重构过程总步数较少，运动模块对本体姿态的影响小。     

椭圆振动切削加工的同步控制研究

利用椭圆振动切削加工的回转体表面微织构是在机床主轴旋转、平台进给和装置椭圆振动的联合运动下得到的,机床主轴转动与装置位移输出如果不能保持完全的实时同步,会造成微织构加工的偏差。为了解决这一问题,提出了一种新的基于转角的同步控制方法,该方法在主轴的对位基准处实现装置正弦信号的复位,以修正转速漂移造成的加工位置误差,并通过判断主轴角度信号实时输出对应幅值电压来实时同步信号输出和机床旋转运动。基于LabVIEW FPGA搭建同步控制模块,经试验验证,利用该方法加工的矩形阵列微织构轴向排列与中轴线平行,与周向排列的夹角和仿真结果的偏差不超过0.1°,织构深度随转角的不同发生预设的变化,因此,同步控制方法具有实时同步控制椭圆振动切削装置的能力。     

Maximal admissible mode decision delay in terminal guidance

Mode decision-maker is a critical component in the logic-based Integrated Estimation and Guidance(IEG) system. For the best possible estimation and guidance performance, the mode decision delay of the mode decision-maker should be limited to a range as small as possible. This paper presents a numerical method for computing the maximal admissible mode decision delay that varies with time-to-go. Particular attention has been paid to highly maneuvering target interception in terminal guidance. The results of this research offer useful guidelines for the design of the mode decision-maker in IEG systems.     

A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs

Angle of Attack (AOA) is a crucial parameter which directly affects the aerodynamic forces of an aircraft. The measurement of AOA is required to ensure a safe flight within its designed flight envelop. This paper intends to summarise a comprehensive survey on the measurement techniques and estimation methods for AOA, specifically in Unmanned Aerial Vehicle (UAV) applications. In the case of UAVs, weight constraint plays a major role as far as sensor suites are concerned. This results in selecting a suitable estimation method to extract AOA using the available data from the autopilot. The most feasible and widely employed AOA measurement technique is by using the Multi-Hole Probes (MHPs). The MHP measures the AOA regarding the pressure variations between the ports. Due to the importance of MHP in AOA measurement, the calibration methods for the MHP are also included in this paper. This paper discusses the AOA measurement using virtual AOA sensors, their importance and the operation.     

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.