MSIS-86和IRI-86模式的扩展应用

本文简要地介绍了MSIS-86和IRI-86两个模式,并用此两模式所给的参数推算出热层中性大气风系、碰撞频率和电离层电导率的时空分布。      

Pattern control for large-scale spacecraft swarms in elliptic orbits via density fields

Space swarms, enabled by the miniaturization of spacecraft, have the potential capability to lower costs, increase efficiencies, and broaden the horizons of space missions. The formation control problem of large-scale spacecraft swarms flying around an elliptic orbit is considered. The objective is to drive the entire formation to produce a specified spatial pattern. The relative motion between agents becomes complicated as the number of agents increases. Hence, a density-based method is adopted...     

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.     

考虑通信延迟的卫星集群改进蜂拥控制

针对卫星集群的无碰撞协同运动问题,提出了一种改进的蜂拥控制策略。首先,结合Prim算法和广度优先算法设计了一种计算度、半径约束最小生成树的拓扑优化方法,并将优化结果作为星群的通信拓扑。该方法不仅能够节省每个卫星的通信资源,还能够减少星群网络的通信延迟。随后,为提高参考轨迹生成的快速性,采用基于傅里叶级数的形状曲线逼近法对主星参考轨迹进行规划。在此基础之上,提出了一种考虑通信延迟与碰撞规避的蜂拥控制策略,使星群能够在保证通信连通性的前提下沿参考轨迹运动到目标点附近,并且在运动过程中不发生碰撞。经过理论分析,利用Lyapunov Razumikhin定理证明了卫星集群系统能够达到渐近稳定。最后,通过数值仿真验证了所提控制策略的正确性和有效性。     

基于强化学习的多无人机避碰计算制导方法

针对大量固定翼无人机在有限空域内的协同避碰问题,提出了一种基于多智能体深度强化学习的计算制导方法。首先,将避碰制导过程抽象为序列决策问题,通过马尔可夫博弈理论对其进行数学描述。然后提出了一种基于深度神经网络技术的自主避碰制导决策方法,该网络使用改进的Actor-Critic模型进行训练,设计了实现该方法的机器学习架构,并给出了相关神经网络结构和机间协调机制。最后建立了一个实体数量可变的飞行场景模拟器,在其中进行"集中训练"和"分布执行"。为了验证算法的性能,在高航路密度场景中进行了仿真实验。仿真结果表明,提出的在线计算制导方法能够有效地降低多无人机在飞行过程中的碰撞概率,且对高航路密度场景具有很好的适应性。     

多弹最优协同诱导突防制导律

针对多弹协同突防问题提出了一种最优协同诱导突防制导律，基于诱导碰撞策略实现有效突防。首先构建了多弹协同对抗非线性模型，并基于零控脱靶量和零控碰撞角进行线性化。然后考虑拦截弹和进攻弹均为二阶驾驶仪动力学特性条件下，将各拦截弹碰撞距离和拦截弹碰撞角扩展到状态方程中，并利用状态转移矩阵降低状态方程维度。在此基础上,设计包含碰撞角约束的快收敛扩展性能指标函数，采用碰撞时间匹配策略推导出多弹最优协同诱导解析制导律的一般形式，并给出2枚进攻弹对2枚拦截弹典型场景下的突防制导律。最后通过典型场景仿真验证了最优协同诱导突防制导律的有效性。     

Extended dynamic system modulation for real-time obstacle avoidance

In this paper, a novel real-time obstacle avoidance method based on Dynamic System (DS), is proposed. The proposed method ensures the impenetrability of multiple convex obstacles by online modulating the original velocity field of the DS. It can be applied to perform obstacle avoidance in the state space of the DS with both autonomous and non-autonomous DS-based controllers. While realizing the obstacle avoidance, the equilibrium points of the original DS can be saved. The modulation matrix form is extended based on the earlier dynamic system modulation methods of the literature. The asymmetric modulation provided by this method allows the modulated DS to satisfy the dynamic constraints of a class of DSs. In addition, the proposed method has the inherent ability of multiple-obstacle avoidance and the direction selectivity of avoidance maneuver. Moreover, to solve the simultaneous guidance and obstacle avoidance problem, a guidance law for Unmanned Aerial Vehicles (UAVs) based on the proposed method, is designed. Finally, a numerical simulation is performed to analyze the performance of the proposed method and the obstacle avoidance guidance law.     

Model predictive control for close-proximity maneuvering of spacecraft with adaptive convexification of collision avoidance constraints

This study investigated model predictive control (MPC) for close-proximity maneuvering of spacecraft. It is essential for a designed MPC to effectively handle collision avoidance between the servicer spacecraft and the client spacecraft, especially while the client is rotating. The rotating motion of the client leads to dynamic changes in the collision avoidance constraints, which increases the difficulty of optimizing the control input in the MPC framework. Therefore, this study presents a method to improve the performance and computational efficiency of MPC for rendezvous and docking with a nonrotating or rotating client. An ellipsoid is adopted to model the client’s keep-out zone (KOZ). Given the spherical KOZ of the servicer, an expanded ellipsoid is introduced to describe the KOZ for the center of mass of the servicer and modeled as a nonlinear constraint. The linearization method for reference points located at the expanded ellipsoid is adopted to convexify the nonlinear constraints. The reference points are adaptively determined according to the positions of the servicer, client, and expanded ellipsoidal KOZ. The resulting hyperplanes are then used to describe the collision avoidance constraints. By utilizing the aforementioned strategies, combined with the calculated reference points, an adaptive convex programming algorithm suitable for real-time implementation of MPC is derived. The performance of the proposed method is demonstrated through numerical simulations.     

航天器反应式碎片规避动作规划方法

提出一种航天器反应式碎片规避动作规划方法，首先以扰动流体动态系统(IFDS)算法作为动作规划的基础算法，通过其中的总和扰动矩阵对航天器的轨道速度矢量进行修正，实现轨道机动规避；然后，建立基于双延迟深度确定性策略梯度(TD3)深度强化学习算法的反应式动作规划方法，通过TD3在线优化IFDS规划参数，实现对碎片群的“状态-动作”最优、快速规避决策。在此基础上，将优先级经验回放和渐进式学习策略引入该方法中，提升训练效率。最后，仿真结果表明，所提方法可使航天器安全规避多发、突发、动态且形状各异的空间碎片群，且具有较好的实时性。