AI 中文总结
该研究提出信息感知MPC框架，将估计协方差纳入控制目标，通过数值模拟验证其能生成满足约束且主动降低目标估计协方差的卫星巡检轨迹。
AI 中文摘要
自主航天器巡检需要满足安全性和控制约束的轨迹，同时能够收集关于目标航天器的有效测量数据。传统制导与控制方法通常将估计与控制解耦，导致轨迹未明确优化感知几何。本研究提出一种模型预测控制（MPC）框架，该框架利用双控制与协方差引导的思路，将估计协方差纳入控制目标。估计协方差根据线性卡尔曼滤波器演化，测量模型取决于代理航天器与目标之间的相对几何关系。通过将协方差动力学嵌入MPC问题，生成的轨迹可考量测量质量，主动减少不确定性，并提升目标估计特征的可观测性。该问题采用Hill-Clohessy-Wiltshire方程描述的相对运动动力学建模，同时施加控制输入、相对距离及终端最大协方差约束。数值模拟表明，所提框架可生成可行的巡检轨迹，在满足代理航天器的输入与安全约束的同时，主动降低目标兴趣点的估计协方差。初始条件的网格分析进一步阐明了轨迹的可行性与价值函数如何依赖于初始条件及约束的激活情况。
英文摘要
Autonomous spacecraft inspection requires trajectories that satisfy safety and control constraints while enabling the collection of informative measurements about a target spacecraft. Traditional guidance and control methods typically decouple estimation from control, resulting in trajectories that do not explicitly optimize sensing geometry. This work presents a model predictive control (MPC) framework that incorporates estimation covariance in the control objective using a formulation inspired by dual control and covariance steering. The estimation covariance evolves according to a linear Kalman filter, and the measurement model depends on the relative geometry between the agent spacecraft and the target. By embedding the covariance dynamics within the MPC problem, the resulting trajectories account for measurement quality, actively reduce uncertainty, and improve observability in the estimated features of the target. The problem is formulated using relative motion dynamics via the Hill-Clohessy-Wiltshire equations with constraints on control input, relative distance, and terminal maximum covariance. Numerical simulations demonstrate that the proposed framework generates feasible inspection trajectories that actively reduce estimation covariance of points of interest on a target while satisfying input and safety constraints of the agent. A mesh analysis of initial conditions further illustrates how feasibility and the value function of the trajectory depend on the initial conditions and constraint activity.
CommentsPresented at the 2026 AAS/AIAA Astrodynamics Specialist Conference in Whistler, BC, Canada. v2 - corrected title metadata, content remains unchanged
