欠驱动N卫星编队的Delta-V最优集中式制导策略
Delta-V-Optimal Centralized Guidance Strategy For Under-actuated N-Satellite Formations
AI总结：
本文针对配备单电推力器的欠驱动N卫星编队，提出基于凸优化的Delta-V最优集中式制导策略，并通过多求解器基准分析给出不同公式的适用条件。
摘要：
本文以集中式方式研究卫星编队Delta-V最优、安全的相对轨道重构计算问题。所考虑的编队包含一个不受控的主航天器和任意数量N个副卫星，其中每个副卫星配备单个电推力器。事实上，这代表了一种正被小卫星平台制造商广泛采用的技术方案。虽然采用单个电推力器确实降低了轨道控制系统所需的功率、重量和尺寸，但其代价是使卫星欠驱动。在这种设置下，卫星只有在执行姿态机动将推力器喷口重新定向到与期望推力方向相反的位置后，才能提供期望的推力矢量。为了进一步扩展此类欠驱动平台的适用范围，本文开发了制导策略以支持N卫星编队的多种重构场景。本文从一个经典的非凸二次约束轨迹优化公式出发，经过多次简化和近似，得到两个新的凸公式，即一个二阶锥规划公式和一个线性规划公式。在本文提出的五种制导公式中，最有前景的三种通过广泛的基准分析进行了比较，该分析应用于十五种最广泛使用的求解器。该基准实验提供了关于不同问题公式之间关键区别的信息，以及在何种条件下可以推荐使用其中每一种公式。
英文摘要：
This paper addresses the computation of Delta-V-optimal, safe, relative orbit reconfigurations for satellite formations in a centralized fashion. The formations under consideration comprise an uncontrolled chief spacecraft flying with an arbitrary number, N, of deputy satellites, where each deputy is equipped with a single electric thruster. Indeed, this represents a technological solution that is becoming widely employed by the producers of small-satellite platforms. While adopting a single electric thruster does reduce the required power, weight, and size of the orbit control system, it comes at the cost of rendering the satellite under-actuated. In this setting, the satellite can provide a desired thrust vector only after an attitude maneuver is carried out to redirect the thruster nozzle opposite to the desired thrust direction. In order to further extend the applicability range of such under-actuated platforms, guidance strategies are developed to support different reconfiguration scenarios for N-satellite formations. This paper starts from a classical non-convex quadratically constrained trajectory optimization formulation, which passes through multiple simplifications and approximations to arrive to two novel convex formulations, namely a second-order cone programming formulation, and a linear programming one. Out of five guidance formulations proposed in this article, the most promising three were compared through an extensive benchmark analysis that is applied to fifteen of the most widely-used solvers. This benchmark experiment provides information about the key distinctions between the different problem formulations, and under which conditions each one of them can be recommended.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
