基于信息一致性的无人机紧密编队集结控制

提出了基于信息一致性的分段式无人机紧密编队集结控制策略,将集结过程分为3步:参考集结点选取和目标集结点分配、形成松散编队以及形成紧密编队。首先,以线切入预定航线的方式计算参考集结点,按照松散编队队形展开生成目标集结点,并利用基于三维距离空间的优化选择算法,将目标集结点快速、准确地分配给每架无人机。然后,使用速度一致性实现向目标集结点定点集结和向松散编队伴航集结,通过非精确的航迹控制快速形成松散编队,提高编队集结的效率。接下来,启动速度、姿态一致性来实现编队最终的精确航迹控制,并逐步压缩编队队形进入紧密编队,避免发生碰撞,完成从松散编队到紧密编队的平稳过渡,同时准确地跟踪预定航线。使用协同修正方法抑制了测量误差、协同误差和通信延迟,提高了紧密编队的稳定性和控制精度。最后,基于MATLAB平台环境对所提三维集结控制策略进行了仿真,验证了其合理性与有效性。     

主-从多飞行器三维分布式协同制导方法

针对三维空间下的多飞行器协同打击问题,基于主飞行器和从飞行器架构提出了两种分布式协同制导方法,实现了所有飞行器最终对目标的同时命中。方法1在定义协调变量的基础上,通过对所选协调变量的一致性协同控制使从飞行器状态同步于主飞行器状态。方法2提出了分布式观测器对主飞行器的状态进行准确估计,各从飞行器通过跟踪观测的主飞行器状态信息实现命中时间的一致。理论分析表明,两种方法都可保证多飞行器同时命中目标。仿真结果验证了所提方法的正确性和有效性。     

可重复使用飞行器的保性能姿态跟踪控制方法

针对可重复使用飞行器再入姿态跟踪控制问题,在考虑执行器饱和、气动参数摄动和外部扰动的情况下,提出了一种保性能姿态跟踪控制方案。通过构造预设性能函数,使姿态跟踪误差在预先设置的包络内演化,保证了系统的瞬态和稳态性能;其次,借助于高增益扩张状态观测器解决了气动参数摄动和外部扰动的问题;之后,基于反步控制框架,设计了一种低复杂度的输出反馈扰动补偿控制方法,保证跟踪误差的收敛性。与已有方法相比,所设计的方法不包含一些复杂的非线性动力学近似技术,如神经网络等,降低了参数调节的复杂性,且无需对虚拟控制律重复微分,避免了"微分爆炸"问题。同时,Lyapunov稳定性分析表明,该方法能够保证误差变量的预期收敛以及其他闭环系统信号的有界性。最后,通过对比仿真验证了所提方法的有效性及可行性。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

网络化飞行器执行机构故障自适应容错控制

针对存在随机短时延和外部干扰的网络化飞行器执行机构故障问题,提出了一种自适应容错控制方法。首先利用扩张状态观测器(Extended State Observer, ESO)对系统不确定性进行估计,并构建了自抗扰控制器对不确定性进行补偿。在此基础上,设计了一种基于跟踪误差的自适应容错控制方法。当执行机构发生未知故障后执行机构指令能自适应逼近设计值,使得重构的控制系统精确跟踪参考模型。最后利用Lyapunov函数方法证明了闭环控制系统的有界稳定。数值仿真校验了所提方法的有效性。     

新一代运载火箭的数据驱动快速测试技术

本文提出一种数据驱动的快速测试技术。数据由箭上系统通过自检测(BIT)采集,经箭地高速总线传送至地面;地面数据处理终端采用与箭上系统相同的模型和算法,复现箭上的处理过程,从而判断系统中是否存在故障,实现快速故障定位。本文全面介绍实现这一设计的关键技术,包括箭上信息监测点的选择、总线窃听技术、飞行软件中的数据管理任务等,并示例说明各类典型数据的分析以及故障诊断和定位等。所有分析工作均实时自动完成,并能随测试用例的调整自适应地计算出新的比对结果,为提高系统级测试的覆盖性创造条件;并大大节省地面专用测试设备和数据分析时间,减少技术保障人员。     

Prescribed fast tracking control for flexible air-breathing hypersonic vehicles: An event-triggered case

In this paper, a prescribed fast tracking control scheme is proposed for Flexible Air-breathing Hypersonic Vehicles (FAHV) subject to lumped disturbances and limited resources. To maintain tracking errors of velocity and altitude converge to a predefined region with a prescribed time and release the transient intense fluctuations encountered in classical Prescribed Performance Control (PPC) using a fast decaying rate, a tracking differentiator-based PPC is presented, where the reaching time and the maximum time differentiation of preselected envelopes can be regulated as a prior via fixing an acceleration factor, so that a guaranteed fast convergence speed can be realized with reduced oscillations. Besides, to avoid the excessive occupation of limited resources (energy and communication) and guarantee a remarkable tracking accuracy, switching event-triggered mechanisms are constructed for FAHV control realization, which provide a promising way to pursue a desired level of tracking performance with a low energy consumption. Subsequently, Uncertainty and Disturbance Estimators (UDE) and Sigmoid function-based Tracking Differentiators (STD) are employed to provide disturbance estimation and reference derivation with a low computational complexity. Finally, robust control laws are designed to compensate for the sampling error induced by event-triggered conditions, meanwhile Zeno phenomena can be effectively eliminated. The simulation results and comparisons validate the effectiveness of the proposed scheme.     

充气式再入航天器总体方案及关键技术初探

充气式再入航天器是一种新型的再入航天器,可实现载人航天和行星探测任务中的行星大气再入,具有系统简单、质量小、气动加热低和可适应不同外形的再入载荷等优点。文章介绍了充气式再入航天器的国际发展现状,并对充气式再入航天器的总体方案进行了初步分析。参考俄罗斯的充气再入与降落技术(inflatable re-entry and descent technology,IRDT)设计先例,提出了充气式再入航天器的构型方案,并针对该构型进行了基于计算流体力学(computational fluid dynamics,CFD)的气动力和热仿真分析。计算采用了迎风格式的层流模型,基于密度进行求解。文章还基于国际空间站的运行轨道,开展了再入轨道的设计,最后对再入气动特性分析、柔性热防护材料、布局与折叠包装和充气机构设计等关键技术提出了初步建议。     

基于遗传算法的再入航天器配重布局优化

提出了一种在遗传算法框架内实现再入航天器配重布局优化的方法,获得的配重分布,可以尽可能小的配重重量代价使再入航天器满足复杂的质量特性要求。该方法将再入航天器可安装配重的区域离散为大量小面积配重区,使每个配重区对应固定质量的配重块,从而对配重布局设计变量进行离散编码,在此基础上建立起再入航天器配重布局优化问题的数学模型,并采用遗传算法求解该优化问题。对某再入航天器示例进行仿真验证,得到优化配重布局方案,以小的配重用量同时满足了质心位置、惯性矩和惯性积的复杂质量特性约束,配重布局显示出复杂空间分布的特点。文章的结果可为各类航天器的配重布局优化提供借鉴。