Stochastic Entry Guidance

TL;DR

This paper develops a stochastic optimal control approach for closed-loop entry guidance in uncertain atmospheres, improving trajectory accuracy by accounting for atmospheric variability.

Contribution

It introduces a novel stochastic guidance method that models entry trajectories as random processes and optimizes control to minimize probabilistic errors.

Findings

Achieved approximately 50% reduction in range errors in simulations.

Demonstrated improved robustness over existing algorithms.

Validated effectiveness in a Mars entry scenario.

Abstract

In this paper, closed-loop entry guidance in a randomly perturbed atmosphere, using bank angle control, is posed as a stochastic optimal control problem. The entry trajectory, as well as the closed-loop controls, are both modeled as random processes with statistics determined by the entry dynamics, the entry guidance, and the probabilistic structure of altitude-dependent atmospheric density variations. The entry guidance, which is parameterized as a sequence of linear feedback gains, is designed to steer the probability distribution of the entry trajectories while satisfying bounds on the allowable control inputs and on the maximum allowable state errors. Numerical simulations of a Mars entry scenario demonstrate improved range targeting performance with approximately 50% lower 1st and 99th percentile final range errors when using the developed stochastic guidance scheme as compared to the existing Apollo final phase algorithm.