论文标题
使用基于抽样的不变漏斗的强大进入车辆指导
Robust Entry Vehicle Guidance with Sampling-Based Invariant Funnels
论文作者
论文摘要
管理不确定性是航天器进入指导中的一个基本问题。本文提出了一种新颖的方法，用于进入进入，下降和着陆期间的不确定性传播，该方法依赖于新的方案稳健验证技术。与基于风险的概率和概率方法不同，我们的技术不依赖任何概率假设。它使用基于设定的描述来绑定不确定性和干扰，例如车辆和大气参数和风。该方法利用了最近开发的基于抽样的平方总编程的版本来计算有限的时间不变性区域，通常称为“不变的funnels”。我们将这种方法应用于三度进入车辆模型，并使用火星科学实验室参考轨迹对其进行测试。我们计算了稳健不变的漏斗的紧密近似值，这些漏斗可以保证以提高着陆精度达到目标区域，同时尊重现实的热约束。
Managing uncertainty is a fundamental and critical issue in spacecraft entry guidance. This paper presents a novel approach for uncertainty propagation during entry, descent and landing that relies on a new sum-of-squares robust verification technique. Unlike risk-based and probabilistic approaches, our technique does not rely on any probabilistic assumptions. It uses a set-based description to bound uncertainties and disturbances like vehicle and atmospheric parameters and winds. The approach leverages a recently developed sampling-based version of sum-of-squares programming to compute regions of finite time invariance, commonly referred to as "invariant funnels". We apply this approach to a three-degree-of-freedom entry vehicle model and test it using a Mars Science Laboratory reference trajectory. We compute tight approximations of robust invariant funnels that are guaranteed to reach a goal region with increased landing accuracy while respecting realistic thermal constraints.
