软着月任务窗口与轨道设计方法研究

建立了一套应用于多约束条件下软着月任务的窗口计算以及轨道设计方法。针对经历发射 段、地月转移段、绕月段及动力下降段的月球探测器,综合考虑各个特征点的位置、光照、 测控等约束,结合轨道特性,采用着月点→近月点→入轨点→发射点倒推的方式,快速确 定探测器的发射窗口。在其中选择发射时刻后即可计算转移轨道的轨道根数初值,并搜索计 算精确轨道。该方法对于我国月球探测后期工程的发射窗口计算与轨道设计工作有较重要的 参考价值。
     

基于能量法的超声速飞航导弹/冲压发动机一体化研究

参考飞机/发动机一体化设计的思路,建立了基于能量法的超声速飞航导弹/冲压发动机一体化设计的约束分析和任务分析模型.简述了超声速飞航导弹任务剖面、约束条件的给定、导弹的质量组成以及冲压发动机安装模型.利用高超声速飞行器的升阻特性,对超声速飞航导弹的升阻特性进行了预测.分析了冲压发动机性能、导弹升阻比对导弹发射总重的影响.约束分析和任务分析的计算结果表明,所建立的一体化约束分析与任务分析模型是合理可行的.     

基于改进扩展分形特征的SAR图像目标检测方法

提出了一种新的纹理特征-改进扩展分形特征用于SAR图像目标检测.改进扩展分形特征通过度量SAR图像中物体表面四个方向的结构函数在双尺度上的增量水平,确定目标与杂波在特征空间的不同响应特性,实现目标的检测.采用模拟目标脉冲模型分析了改进扩展分形特征在不同信噪比条件下对不同尺寸目标的响应特性,分析结果表明改进扩展分形特征具有目标尺寸敏感性.利用SAR实测数据验证了改进扩展分形特征在强杂波和多目标环境下的优良检测性能与空间分辨率特性.     

对空间碎片的相对位姿估计

针对几何、质量特性参数均未知的空间碎片,提出一种基于扩展卡尔曼滤波（EKF）的相对位姿估计算法。该算法以单目相机获得的目标图像特征视线信息作为测量输入,以目标姿态、姿态角速度、惯量比、相对位置、相对速度和特征位置作为滤波状态。对算法进行可观性分析,给出两个可以保证算法可观性的条件。分别针对不同条件进行数值仿真,仿真结果表明新算法能够对几何和质量参数未知的空间碎片进行有效的相对位姿 估计。     

采用单目视觉的非合作目标星状态估计

针对微小卫星逼近观测未知的空间翻滚非合作目标星任务,提出一种基于单目视觉的目标星相对状态估计方法。在建立追踪星/目标星相对运动模型的基础上,以单目相机识别并测量获得的目标星固有特征的像素位置为观测输入,通过扩展卡尔曼滤波算法实现对目标星相对位置、相对速度、相对姿态、角速度、惯量比和特征位置等状态的估计。仿真结果表明,该方法能够很好地实现对未知非合作目标星的相对状态估计,姿态估计误差小于2°,位置估计误差小于0.1 m,特征点位置平均估计误差小于0.04 m。     

X射线天文卫星观测需求分析与控制总体设计

对X射线天文卫星观测需求进行了分析,提炼了观测任务对观测模式、源的高精度定位与对准、轨道、热控、测控数传等多项需求与约束;针对X射线观测的多需求、多约束难点,设计了集巡天观测、定点观测与小天区扫描观测于一体的观测模式,解决了一颗卫星同时实现全天扫描、银道面深度扫描、重要惯性区域扫描、重要及机遇目标深度观测以及伽马暴全天监测的多种观测需求的难题,该技术已在我国硬X射线调制望远镜卫星上得到应用.     

Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping

This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos.     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

月面无人自动采样返回飞行程序设计与实现

针对嫦娥五号飞行程序多舱段、多任务、系统控制复杂的设计特点和难点,采用传统的飞行程序设计方法工作量大,状态控制困难,很难满足任务要求。提出了一种新的基于状态转移的方法对飞行程序进行系统建模,首先将整个飞行过程分解成若干模块状态机;然后针对每个模块状态机的功能划分为状态触发器、评估器、执行器和确认器,并分别开展建模设计;最后通过状态触发器和确认器将各个功能模块进行连接,形成整个飞行程序的有限状态机描述。相比传统方法,该方法具有通用性、可扩展性和可复用性等特点,对于规范飞行程序设计,描述复杂的飞行任务过程有很大的优势。采用该方法对嫦娥五号飞行程序进行了建模和设计,并给出了典型飞行过程的设计结果。在轨飞行试验结果表明,该方法可以满足飞行任务的要求,确保了嫦娥五号在轨飞行控制任务圆满成功。     

基于有向图模型的卫星任务指令生成算法

面向任务的卫星操控模式具有操作简便、星载资源使用效率高的优点,正在取代指令序列注入成为遥感卫星运控的新模式。文章提出一种基于有向图模型的遥感卫星任务指令序列生成算法,具有线性存储复杂度和计算复杂度,适合存储资源和计算资源受限的星载计算机应用。此算法已在某遥感卫星应用,测试试验表明,采用3个面向任务的高级指令即可生成52种指令序列,任务上行注入效率提升了5倍。本文方法根据有效载荷的使用约束条件,配置有向图模型参数,即可满足各种类型卫星使用。