Parachute dynamics and perturbation analysis of precision airdrop system

To analyze the parachute dynamics and stability characteristics of precision airdrop system,the fluid–structure interaction(FSI) dynamics coupling with the flight trajectory of a parachute–payload system is comprehensively predicted by numerical methods.The inflation behavior of a disk-gap-band parachute is specifically investigated using the arbitrary Lagrangian–Euler(ALE) penalty coupling method.With the available aerodynamic data obtained from the FSI simulation,a nine-degree-of-freedom(9DOF) dynamic model of a parachute–payload system is built and solved to simulate the descent trajectory of the multi-body dynamic system.Finally,a linear five-degree-of-freedom(5DOF) dynamic model is developed,the perturbation characteristics and the motion laws of the parachute and payload under a wind gust are analyzed by the linearization method and verified by a comparison with flight test data.The results of airdrop test demonstrate that our method can be further applied to the guidance and control of precision airdrop systems.     

A novel biased proportional navigation guidance law for close approach phase

A novel biased proportional navigation guidance (BPNG) law is proposed for the close approach phase, which aims to make the spacecraft rendezvous with the target in specific relative range and direction. Firstly, in order to describe the special guidance requirements, the concept of zero effort miss vector is proposed and the dangerous area where there exists collision risk for safety consideration is defined. Secondly, the BPNG, which decouples the range control and direc-tion control, is designed in the line-of-sight (LOS) rotation coordinate system. The theoretical anal-ysis proves that BPNG meets guidance requirements quite well. Thirdly, for the consideration of fuel consumption, the optimal biased proportional navigation guidance (OBPNG) law is derived by solving the Schwartz inequality. Finally, simulation results show that BPNG is effective for the close approach with the ability of evading the dangerous area and OBPNG consumes less fuel compared with BPNG.     

超声速混合层在强迫振动下流场结构的实验研究

通过可控振动系统激励双喷管后的薄平板,采用基于纳米粒子的平面激光散射技术(NPLS)观测超声速混合层流场结构。控制薄平板的频率和振幅,结合边缘检测技术研究了超声速混合层大尺度结构的发展和演化规律。实验结果表明:强迫振动对超声速混合层流场结构以及混合效率有重要的影响。与无振动相比,强迫振动增强了混合层的三维特性,混合层的失稳位置提前;同时,强迫振动促进了上下两层流动的掺混,高频振动下混合层增长速度提高约50%,有效增强了混合。     

基于自适应网格的超声速液体射流破碎过程研究

为研究超声速气流中液体横向射流的破碎过程,采用脉冲背景光方法和VOF方法开展了实验和数值研究。为提高液体横向射流中气液界面和气流场特征捕捉的精确性,采用自适应网格技术对于气液界面、激波出现位置进行网格细化,计算得到了较为精细的气液界面、激波特征及涡系结构。研究结果表明:在低成本仿真模拟条件下,利用自适应网格计算得到的射流轨迹和轮廓与实验吻合较好,射流轨迹的最大误差为10%;射流初始段在超声速气流条件下,仍然存在一段高度约为1.9倍喷孔直径且圆柱形态保持较好的连续光滑液柱。随着喷注压降的升高,液柱的长度逐渐增大;主流气体流经液柱发生三维绕流,在射流附近和近壁面区域形成不断演化的反转涡对,反转涡对的形成加速了液体射流一次破碎过程。     

基于纯比例导引的拦截碰撞角约束制导策略

拦截碰撞角约束制导是当前导弹制导研究的关键问题之一。首先基于理想比例导引（IPN）律拦截非机动目标的解析解,推导了纯比例导引律（PPN）拦截固定目标的解析解,得到了弹目相对距离、制导指令加速度和导弹前置角的显示表达式,并进一步得到了拦截碰撞角与弹目相对运动状态和比例导引系数之间的解析表达式。其次,基于该解析表达式,提出了基于PPN的拦截碰撞角约束制导策略（PPNIACG）,并探讨了在铅垂面内进行落角约束打击和水平面内进行拦截碰撞角约束打击的2种实现方式。最后,以弹道成型制导律（TSG）和最优碰撞角约束制导律（OIACG）为参考,通过数值仿真算例,对PPNIAC的拦截性能进行了对比分析,验证了所提出制导策略的有效性和正确性。     

小型固定翼无人机集群综述和未来发展综述

围绕小型固定翼无人机集群这一难度高、发展快、应用前景广阔、多学科交叉的新方向,从集群系统内涵、现有典型项目、关键技术3个角度综述了国内外小型固定翼无人机集群的研究现状。在系统梳理集群系统内涵和应用优势的基础上,从集群协同模式探索、分布指挥体系构建、核心关键技术突破和集群验证等4个视角总结现有典型项目,从体系架构、通信组网、决策与规划、飞机平台、集群飞行、集群安全与集群指控等7个核心点综述了技术研究现状。最后,综合小型固定翼无人机集群中亟需解决的关键技术,展望了这一领域未来的发展趋势。     

多飞行器再入段时间协同弹道规划方法

提出了一种多飞行器再入段时间协同弹道规划方法。首先,在纵向平面内规划满足航程与终端约束的纵向标称轨迹。随后,在采用轨迹跟踪律跟踪纵向标称轨迹的同时,运用考虑初始横侧向状态的多边界航向偏差角走廊策略控制飞行器的横侧向机动,以满足到达时间约束与终端约束,进而实现单枚飞行器到达时间约束下的轨迹规划。在此基础上,完成了飞行器的到达时间分布与飞行能力分析,给出了最小与最大到达时间的分析计算方法,并根据多飞行器协同再入的任务需求完成了协同飞行时间决策。最后,多飞行器协同再入与扰动条件下的仿真结果表明,该方法能够规划出满足到达时间与终端约束的协同再入轨迹,具备良好的计算精度与鲁棒性。     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.     

Optimal guidance for reentry vehicles based on indirect Legendre pseudospectral method

Development of a feasible guidance scheme for reentry vehicles is a challenge because of its significant nonlinearity and multi-constraints. A method for the implementation of three-degree-of-freedom guidance for constrained reentry vehicle is presented in the paper. First, the constrained trajectory is generated by Legendre pseudospectral method (LPM) and then the feasibily of the trajectory is validated. Based on the obtained reference trajectory, the guidance problem is converted into a trajectory state regulation problem which is a linear time varying system. A robust state feedback guidance law is generated in real time using indirect Legendre pseudospectral feedback method. Finally, simulation results illustrate that the overall guidance scheme can lead to a very accurately controlled flight with all the constraints satisfied even in the presence of initial state uncertainty.     

Regularized robust filter for attitude determination system with relative installation error of star trackers

As one of the most critical issues for high-accuracy satellite attitude determination, the relative installation error of star tracker usually leads to inconsistency of the output attitude information. In this paper, an approach named regularized robust filter algorithm is proposed to control the relative installation error of star tracker in the attitude measurement data. Based on the uncertainty model established for the attitude measurement system, the weighted least square solution is presented and the regularized robust filter is deduced firstly. The algorithm parameters are then optimized with the design indices in order to minimize the upper boundary for the variance of the estimated error. Compared with the traditional Kalman filter, the regularized robust filter takes into consideration the effects of model uncertainty, which can be used to optimize the filter parameters during its design stage. Thus, the information of both the system model and the measurement data can be applied effectively. Moreover, the existence conditions need not be validated in the proposed filter algorithm, which is convenient for on-orbit application. Finally, simulation results demonstrate the validity and efficiency of the proposed method. The relative installation error of attitude determination is mostly reduced and the estimation precision is improved greatly.