This paper presents an integrated model-learning predictive control scheme for spacecraft orbit-attitude stationkeeping 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.

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

Bernelli Zazzera F.
2022

Abstract

This paper presents an integrated model-learning predictive control scheme for spacecraft orbit-attitude stationkeeping 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.
File in questo prodotto:
File Dimensione Formato  
SANCJ01-22.pdf

Accesso riservato

Descrizione: Paper
: Publisher’s version
Dimensione 3.95 MB
Formato Adobe PDF
3.95 MB Adobe PDF   Visualizza/Apri
SANCJ_OA_01-22.pdf

embargo fino al 01/03/2022

Descrizione: Paper Open Access
: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 8.23 MB
Formato Adobe PDF
8.23 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1201501
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact