双星定位／SINS组合导航系统研究

双星定位系统是一种区域性定位系统。介绍了双星定位系统的定位原理，说明了其存在的定位实时性较差的不足。对此提出了利用惯导经卡尔曼滤波器校正后的输出速度信息来修正双星定位系统的输出水平位置信息，然后与惯导进行组合，建立了其状态方程与观测方程。仿真结果证明该方案切实可行，对双星定位系统的实际应用有重大的理论参考价值。     

频率源对雷达测速精度影响分析

针对高稳频率源如何影响雷达测速精度问题,构建基于频率源分析雷达测速精度的理论模型,利用同步校频和互备双锁相环(PLL)改进方案实现了高稳高可靠频率源,然后通过静态模拟试验和动目标跟踪试验,分析频率源幅度、准确度、稳定度及同步校频、锁相环等模块对多普勒和测速精度的影响。结果表明,锁相环能够提升频率源短稳,降低雷达测速随机误差,同步校频能够减小本地铷钟与外部基准的频差,减小雷达测速系统误差,通过双源和双锁相环的设计,频率源切换或锁相环路切换时,也能够保证测速雷达系统正常工作。     

WR 技术的发展与应用

本文介绍一种在精密时钟同步协议（PTP）的基础上发展而来的新技术——WR 技术（White Rabbit，白兔）。WR 联合同步以太网以及双混频时差法测量手段，通过对主从时钟间链路环路延迟的不间断高精度的测量，后以测量结果对从时钟的时钟信息进行动态校准，主从时钟的同步精度达到亚纳秒甚至皮秒级。文章先引出WR的发展历史，以及WR 中所包含的主要技术，并介绍目前WR 在某些科学领域所发挥的作用，最后提出WR 未来在其他领域的应用。     

铝合金振动疲劳同步测量技术研究

随着振动试验技术的不断进步,振动疲劳试验拥有了良好的试验基础,能够进行各种环境下的振动疲劳模拟试验,但目前振动疲劳的测量方法还无法满足振动疲劳试验深入研究的需求。提出铝合金振动疲劳同步测量的方法,同时采用红外监测技术、声发射技术、显微测试技术、应变测试技术等对不同加速度下的铝合金振动疲劳试样进行测量,研究各种测量参数随损伤的变化情况。结果表明:铝合金振动疲劳不同阶段应主要考虑不同的测量参数作为判断损伤的依据,声发射信号可以很好检测到铝合金振动疲劳的裂纹萌生和扩展寿命,红外测得的温升值随着加速度增大是非线性的,铝合金振动疲劳寿命依赖于结构振动频率、阻尼等参数。     

卫星正常模式姿态确定算法研究

针对太阳同步极轨卫星进行了正常模式姿态确定算法研究 ,采用四元数法建立了较完整和准确的姿态估计器模型 ,并进行了可观测性及观测度的分析。通过数学仿真证明此姿态确定算法能够对星体姿态进行较精确的估计或校正 ,满足卫星控制精度要求     

同步卫星姿态控制系统稳定性分析

本文介绍苏联“萤光屏”系列地球同步卫星的姿态控制系统以及在稳态运行时姿态控制系统俯仰通道的稳定性分析,分析中考虑了太阳帆板的挠性。飞行实验表明,文中所介绍的方法在工程设计中是简单可行的。     

以单片机为基础的并行相关帧同步器

介绍了一种采用并行相关技术实现的可变速率、可变帧长的帧同步器。它以８０３１单片机为基础采用硬件、软件相结合的方法实现主帧、副帧同步。并根据理论计算，得出容错门限、校核帧数。和保护帧数卢。该帧同步器已成功地运用于中国通信卫星ＤＦＨ－３在主动轨道段和过渡轨道段遥测数据的解调。     

卫星瞬态温度场的计算分析

本文讨论了地球同步通信卫星热设计中的几个典型瞬态溫度问题,采用Stiff稳定的自动积分法,对250阶的非线性微分方程组进行数值求解,在校正迭代计算中配合以稀疏矩阵的技术,获得了良好的效果。     

Bifurcation analysis of dual-sidestay landing gear locking performance considering joint clearance

Sidestay lock mechanism is an important part of landing gear system, and the locking performance can be analyzed based on changes in its stability. However, during numerical continuation analysis of fully-rigid dual-sidestay landing gear without clearance, it has been found that the appearance of bifurcation points does not necessarily imply that both sidestay links can be locked synchronously. This problem reveals the limitations of fully-rigid model with ideally-articulated in solving dual-sid...     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.