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Relative Sensing, Control Precision, and Mission Delta-V Trade-Offs for Precision Formation Flying in Planetary Orbit

机译:行星轨道上精确编队飞行的相对传感,控制精度和任务Delta-V折衷

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摘要

As control precision for Earth-orbiting formations increases, the required Delta V can quickly become infeasible. Missions have demonstrated approximately 5 m/s/yr for 10-m control (TanDEM-X), but that quickly grows to approximately 90 m/s/yr for 1.5-m control (CanX-4/5). Previous research has shown that formation reference trajectories must use high-fidelity relative orbital dynamics; otherwise, Delta V is spent rejecting modeling errors. However, even with a perfect reference trajectory, feeding back relative state estimation errors can drive Delta V. This paper presents a linear time-invariant analysis approach that quantifies the three-way trade-off between mission Delta V, control precision, and estimation error for approximately circular orbits and verifies the approach through high-fidelity simulations, including tracking an ideal reference trajectory that eliminates the Delta V cost of modeling errors. Because Linear Quadratic Regulator control is used, the resulting Delta V can be considered a lower bound on the required Delta V, assuming linear control and quadratic cost. Using reference trajectories based on simplified dynamics, such as without differential J2, would only increase Delta V. Using this analysis approach, for example, the minimum required capability of a relative sensing and estimation system could be determined given a mission concept's Delta V capability and control requirements for science.
机译:随着对地球轨道编队的控制精度的提高,所需的Delta V可能很快变得不可行。任务已经证明,对于10米控制(TanDEM-X),每年大约5 m / s / yr,但是对于1.5米控制(CanX-4 / 5),它很快增长到大约90 m / s / yr。先前的研究表明,编队参考轨迹必须使用高逼真度的相对轨道动力学。否则,Delta V将被用于拒绝建模错误。但是,即使具有理想的参考轨迹,反馈相对状态估计误差也可以驱动DeltaV。本文提出了一种线性时不变分析方法,该方法量化了任务Delta V,控制精度和估计误差之间的三路权衡。用于近似圆形的轨道,并通过高保真度仿真验证了方法,包括跟踪理想的参考轨迹,从而消除了建模误差的Delta V成本。因为使用了线性二次调节器控制,所以假设线性控制和二次成本,所得的增量V可以视为所需增量V的下限。使用基于简化动力学的参考轨迹(例如不使用差分J2)只会增加DeltaV。例如,使用这种分析方法,可以根据任务概念的Delta V能力确定相对感测和估计系统的最低要求能力,并且科学的控制要求。

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