无人机编队支撑网络的协同通信中继策略

针对无人机编队支撑网络优化选择中继成员进而提高编队协同通信能力问题，基于端到端技术（Device-to-Device，D2D），提出了一种同时考虑无人机编队成员的邻近域及编队成员间的社会性依存关系来优化选择中继成员的中继策略，旨在既能保证无人机编队的基本通信能力，又能同时优化提高编队的吞吐量，从而进一步提升编队支撑网络的整体性能。研究结果表明，所提的无人机编队支撑网络中继成员优化选择策略在系统吞吐量方面较已有方法具有明显优势。     

基于循环追踪控制的卫星编队构形调整控制律设计

卫星编队控制问题中，分布式控制优于主从式控制，在编队控制中应用日益广泛。提出了一种基于循环追踪算法的分布式控制策略，分析了该方法的优势。由于循环追踪算法存在编队中心由初始几何中心固定并与运动过程无关的弱点，引进了虚拟灯塔导引进行联合控制实现编队中心可变。建立了三维空间相对运动数学模型，设计非线性循环追踪算法与虚拟灯塔导引联合控制律，对全员联合控制与单星联合控制其余卫星采用非线性循环追踪控制两种方案的计算结果进行比较。结果表明，全员联合控制律的控制方案优于仅单星采用联合控制的方案，两种方案均可实现卫星编队按指定构形运动。     

一种基于双目视觉的卫星相对位姿测量方法

卫星位姿测量技术是与航天飞行器相关的空间操控活动中一项核心的支撑技术.考虑到太空环境中光源单一，且卫星表面一般为反光材质，提出了一种基于双目视觉的卫星相对位姿测量方法.在卫星面板标志点可见时，本文方法利用卫星对接环外环和卫星面板标志点测量卫星相对位姿.在卫星距离对接目标较近，无法观测到卫星面板标志点时，本文方法利用ORB特征点匹配测量位姿.为了增强鲁棒性，本方法还利用光流追踪法和卡尔曼滤波器优化位姿测量的结果.仿真实验结果表明，本方法能够在光源单一背景下对任意种类的卫星在对接过程中进行准确的位姿测量.     

无人机参数优化自抗扰编队保持控制器设计

针对无人机编队飞行中的队形保持问题,采用结合粒子群算法的自抗扰控制技术对控制器进行设计,使响应过程具有良好的动态品质和较小的稳态误差。建立三维空间内基于相对误差的无人机编队飞行模型,并在此基础上设计了跟踪微分器、扩展状态观测器和非线性状态反馈自抗扰控制器,采用粒子群算法对控制器中部分参数进行优化整定。仿真结果表明,设计的结合粒子群算法的改进自抗扰控制器具有良好的控制性能,能以较高的精度实现无人机编队保持任务。     

无拖曳卫星氮气微推进系统填充与开机工作特性的仿真研究

采用AMESim软件对氮气贮存压力为1.5×10 7 Pa、推力范围为mN级的压电驱动的氮气微推进系统进行建模。研究了氮气填充过程中氮气瓶、减压阀的压力和质量流量瞬态特性。分析了整合喷管的压电比例阀在开机过程中的瞬态工作特性。最后，研究了驱动电压对压电比例阀在开机过程中的阀芯位移和喷管推力瞬态值、阀芯运动和推力响应时间的影响规律。结果显示，当驱动电压为80 V时，阀芯的响应时间和稳定位移分别为 0.64 ms 和3.67 μm。开机后8 ms，喷管推力达到稳定值(0.588 mN)。压电比例阀阀芯的开启响应快速，且驱动电压与喷管推力之间存在良好的线性关系，说明推力可通过改变驱动电压进行mN级的线性调节。     

基于分布式自适应的多智能体容错一致性控制

针对"领航者-跟随者"的多智能体编队，由于领航者系统出现故障引起编队通讯中断而不能完成任务的问题，提出了一种基于一致性理论的分布式自适应控制方法，用于解决该问题。首先，以一个位于顶点的智能体作为领航者，其余3个位于同一条线上的智能体作为跟随者，由此所构成的三角形编队作为被控对象。其中，领航者速度方向作为编队的前行方向，跟随者位于领航者之后。其次，基于图论，对智能体局部信息参数进行分布式自适应更新，并设计分布式自适应控制律，用于弥补多智能体编队中领航者故障所造成的影响。同时，根据相邻智能体的局部信息，设计整体分布式自适应容错控制律，进一步通过构建合理的Lyapunov函数，证明所设计控制器的稳定性，以及"领航者-跟随者"之间相对横向以及相对纵向的距离误差均收敛于固定常数。最后，仿真验证表明：所提出的自适应控制方法具有良好的鲁棒性，这也为工程实践提供了理论依据。     

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.     

An LMI-based decoupling control for electromagnetic formation flight

Electromagnetic formation flight(EMFF) leverages electromagnetic force to control the relative position of satellites. EMFF offers a promising alternative to traditional propellant-based spacecraft flight formation. This novel strategy is very attractive since it does not consume fuel. Due to the highly coupled nonlinearity of electromagnetic force, it is difficult to individually design a controller for one satellite without considering others, which poses challenges to communications.This paper is devoted to decoupling control of EMFF, including regulations, constraints and controller design. A learning-based adaptive sliding mode decoupling controller is analyzed to illustrate the problem of existing results, and input rate saturation is introduced to guarantee the validity of frequency division technique. Through transformation, the imposed input rate saturation is converted to state and input constraints. A linear matrix inequalities(LMI)-based robust optimal control method can then be used and improved to solve the transformed problem. Simulation results are presented to demonstrate the effectiveness of the proposed decoupling control.     

Using only observation station data for PPP ambiguity resolution by UPD estimation

This article proposes a new method for uncalibrated phase delay (UPD) estimation to improve the accuracy of precise point positioning (PPP), which uses only observation station data. This means that the station used to generate the UPDs is the same station to which they are applied. First, dual-frequency observation equations based on a raw PPP model are developed. Then, the UPDs are calculated from integer linear combinations of float ambiguities. Third, with the UPD corrections, the least-squares ambiguity decorrelation adjustment (LAMBDA) method is utilized to obtain the integer ambiguities. Since only observation station data are used for UPD estimation, the partial ambiguity resolution (PAR) method is adopted to increase the possibility of finding a subset of integer ambiguities. The UPD estimation and ambiguity resolution are performed in each epoch. To obtain the correct integer ambiguity, the ratio test and success rate (bootstrapping) are used to evaluate the estimated integer ambiguity. Finally, by treating the integer ambiguities as constants, fixed solutions can be obtained. Quality control is also applied throughout the entire data processing procedure to obtain high quality float and fixed solutions. Data from 22 stations of the International Global Navigation Satellite System (GNSS) Service (IGS) in East Asia on day of year (DOY) 206, 2017, are used to verify the feasibility of this method. The experimental results show that compared with the float solution, the proposed method can significantly improve the accuracy in the east, north and up directions by 24%, 21% and 18% for static PPP and 36%, 18% and 34% for dynamic PPP, respectively. However, the accuracy of the proposed method is still lower than that of the fixed solutions obtained by the PRIDE-PPPAR software, in which the fractional cycle bias is computed based on reference network data. These findings sufficiently show that the proposed method can offer better solution accuracy than the float solution. However, the quality of the UPDs estimated only from observation station data is not as good as that of the estimates obtained based on reference network data.     

Connectivity preservation and collision avoidance control for spacecraft formation flying in the presence of multiple obstacles

This paper addresses connectivity preservation and collision avoidance problem of spacecraft formation flying with multiple obstacles and parametric uncertainties under a proximity graph. In the proximity graph, each spacecraft can only get the states of the neighbor spacecraft within its sensing region. Connectivity preservation of a graph means that the connectivity of the graph should be preserved at all times during spacecraft formation flying. We consider two cases: (i) the obstacles are static, and (ii) the obstacles are dynamic. In the first case, a distributed continuous control algorithm based on artificial potential function and equivalent certainty principle is proposed to account for the unknown parameters and the static obstacles. In the second case, a sliding surface combined with a distributed adaptive control algorithm is proposed to tackle the influence of the dynamic obstacles and the unknown parameters at the same time. With the distributed control algorithms, the desired formation configuration can be achieved while the connectivity of the graph is preserved and the collisions between the spacecraft and the obstacles are avoided. Numerical simulations are presented to illustrate the theoretical results.