Orbit-Attitude Predictive Control in the Vicinity of Asteroids with In Situ Gravity 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 unkown 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 allow for a high degree of accuracy since the

Data-Driven Guidance and Control for Asteroid Landing Based on Real-Time Dynamic Mode Decomposition

“…Φ is a matrix composed of the eigenvectors φ k and Ω is a diagonal matrix with the eigenvalues ω k . For a continuous-time dynamical system of the form (14), it is always possible to derive a discrete-time linear system using the sampling time t as follows:…”

“…Guidance and control in the microgravity environment near asteroids present significant challenges due to strong uncertainties arising from non-uniform gravitational fields caused by asteroid shapes, as well as surface roughness and control errors. Designing missions for unvisited small bodies [14] is particularly challenging as available information about the targets is limited in advance. Accurately determining the shape and gravitational field of asteroids requires a long-time in-situ characterization campaign that largely relies on Earth ground segment data processing.…”