TY - GEN
T1 - Differential-drag algorithms for satellite cluster flight in the Samson mission
AU - Ben Yaacov, Ohad
AU - Gurfil, Pini
PY - 2015
Y1 - 2015
N2 - The idea of differential drag (DD) as a means for fuelless satellite cluster keeping emerged in the mid-Eighties, when the feasibility of DD-based control was proven assuming linearized relative dynamics for two satellites. The present work develops a new DD-based cluster keeping method suitable for implementation in long-term cluster flight missions consisting of multiple satellites. This algorithm has been implemented on the nanosatellites planned to be launch as part of the Space Autonomous Mission for Swarming and Geo-locating Nanosatellites (SAMSON). Obviously, any drag-based algorithm must cope with aerodynamical and mechanical uncertainties. The overall error related to drag calculation is inevitable and could be as high as one or two orders of magnitude, which can be crucial for any drag-based control. Hence, a covariance analysis of the closed-loop system was developed, in the presence of drag uncertainties, initial condition-related uncertainties and measurement noise. A Kalman filter is designed in order to generate inputs to the differential drag controller. The variance of the differential mean semimajor axis is propagated analytically using the Linear Covariance Analysis (LCA) technique, which enables to propagate the augmented state and filter covariance without propagating the state itself. The results show that all these uncertainties have relatively small affect on the inter-satellite distance, even for long term, which prove the robustness of the differential drag controller.
AB - The idea of differential drag (DD) as a means for fuelless satellite cluster keeping emerged in the mid-Eighties, when the feasibility of DD-based control was proven assuming linearized relative dynamics for two satellites. The present work develops a new DD-based cluster keeping method suitable for implementation in long-term cluster flight missions consisting of multiple satellites. This algorithm has been implemented on the nanosatellites planned to be launch as part of the Space Autonomous Mission for Swarming and Geo-locating Nanosatellites (SAMSON). Obviously, any drag-based algorithm must cope with aerodynamical and mechanical uncertainties. The overall error related to drag calculation is inevitable and could be as high as one or two orders of magnitude, which can be crucial for any drag-based control. Hence, a covariance analysis of the closed-loop system was developed, in the presence of drag uncertainties, initial condition-related uncertainties and measurement noise. A Kalman filter is designed in order to generate inputs to the differential drag controller. The variance of the differential mean semimajor axis is propagated analytically using the Linear Covariance Analysis (LCA) technique, which enables to propagate the augmented state and filter covariance without propagating the state itself. The results show that all these uncertainties have relatively small affect on the inter-satellite distance, even for long term, which prove the robustness of the differential drag controller.
UR - http://www.scopus.com/inward/record.url?scp=84994247293&partnerID=8YFLogxK
M3 - منشور من مؤتمر
T3 - Proceedings of the International Astronautical Congress, IAC
SP - 5954
EP - 5963
BT - 66th International Astronautical Congress 2015, IAC 2015
T2 - 66th International Astronautical Congress 2015: Space - The Gateway for Mankind's Future, IAC 2015
Y2 - 12 October 2015 through 16 October 2015
ER -