基于自适应通信拓扑的无人机集群弱路径约束下的分布式控制器设计

针对传统无人机集群飞行控制需要规划过多飞行路径的问题，设计一种结合线性二次型调节器(LQR)与机间防撞的分布式协同控制器，不需要为集群设计相对构型，只需给定一条满足无人机动力学约束的飞行路径，集群就可沿路径飞行。该控制器通过设计自适应通信拓扑来稳定加入机间防撞机制带来的抖振效应，并使集群具有良好可扩展性。仿真结果表明，所设计的分布式控制器可以实现规划一条路径下无人机集群无机间碰撞的稳定飞行。     

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。