Orbit-Attitude Predictive Control in the Vicinity of Asteroids with In Situ Gravity Estimation

TL;DR

This paper introduces a predictive control method for spacecraft near asteroids that simultaneously estimates gravity in situ, improving orbit-attitude stability through adaptive learning and model refinement.

Contribution

It presents a novel integrated model-learning predictive control scheme that estimates asteroid gravity and controls spacecraft in real-time, enhancing station-keeping accuracy.

Findings

Gravity model identification improves over time

Tracking errors decrease as model accuracy increases

Constellation mission accelerates gravity model estimation

Abstract

This paper presents an integrated model-learning predictive control scheme for spacecraft orbit-attitude station-keeping in the vicinity of asteroids. The orbiting probe relies on optical and laser navigation while attitude measurements are provided by star trackers and gyroscopes. The asteroid gravity field inhomogeneities are assumed to be unknown a priori. The state and gravity model parameters are estimated simultaneously using an unscented Kalman filter. The proposed gravity model identification enables the application of a learning-based predictive control methodology. The predictive control allows for a high degree of accuracy because the predicted model is progressively identified in situ. Consequently, the tracking errors decrease over time as the model accuracy increases. Finally, a constellation mission concept is analyzed in order to speed up the model identification process. Numerical results are shown and discussed.