Atmospheric Density-Compensating Model Predictive Control for Targeted Reentry of Drag-Modulated Spacecraft

TL;DR

This paper introduces an estimation and control framework combining an EKF and MPC to enable targeted spacecraft reentry despite atmospheric density uncertainties, demonstrating high accuracy in simulations with real space weather data.

Contribution

It develops a novel integrated EKF-MPC approach for atmospheric density compensation in spacecraft reentry, improving robustness and tracking accuracy over existing methods.

Findings

Achieves 98.4% of cases within 100 km of the guidance trajectory.

Guides spacecraft to entry interface with a mean error of 12.1 km.

Framework remains effective despite large density errors during solar storms.

Abstract

This paper presents an estimation and control framework that enables the targeted reentry of a drag-modulated spacecraft in the presence of atmospheric density uncertainty. In particular, an extended Kalman filter (EKF) is used to estimate the in-flight density errors relative to the atmospheric density used to generate the nominal guidance trajectory. This information is leveraged within a model predictive control (MPC) strategy to improve tracking performance, reduce control effort, and increase robustness to actuator saturation compared to the state-of-the-art approach. The estimation and control framework is tested in a Monte Carlo simulation campaign with historical space weather data. These simulation efforts demonstrate that the proposed framework is able to stay within 100 km of the guidance trajectory at all points in time for 98.4% of cases. The remaining 1.6% of cases were pushed away from the guidance by large density errors, many due to significant solar storms and flares, that could not physically be compensated for by the drag control device. For the successful cases, the proposed framework was able to guide the spacecraft to the desired location at the entry interface altitude with a mean error of 12.1 km and 99.7% of cases below 100 km.