Robust Entry Vehicle Guidance with Sampling-Based Invariant Funnels
This addresses the critical problem of managing uncertainty in spacecraft entry, descent, and landing for space missions, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles uncertainty propagation in spacecraft entry guidance by developing a sum-of-squares robust verification technique that uses set-based descriptions to bound uncertainties without probabilistic assumptions, achieving increased landing accuracy while respecting thermal constraints in Mars Science Laboratory simulations.
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.