2016
DOI: 10.2514/1.a33332
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Differential Drag-Based Reference Trajectories for Spacecraft Relative Maneuvering Using Density Forecast

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Cited by 17 publications
(8 citation statements)
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“…E If a propulsion system is not an option (due to cost or volume constraints), changing the aerodynamic drag the spacecraft experiences through modulation of the ballistic coefficient presents itself as the most feasible way to perform re-entry targeting. While extensive work has been done on density modeling and spacecraft drag estimation 8,9 , and there is a body of research focused on relative spacecraft maneuvering using differential drag [10][11][12] , very little research has been conducted on a de-orbit algorithm that utilizes solely aerodynamic drag to control the decay trajectory. Though the modulation of vehicle aerodynamics has been utilized since the Apollo missions 4 to help vehicles maintain a precomputed guidance during the re-entry trajectory, the use of solely aerodynamic drag for reentry targeting presents a much greater challenge and has never been done in practice before to the best of the authors' knowledge.…”
Section: Introductionmentioning
confidence: 99%
“…E If a propulsion system is not an option (due to cost or volume constraints), changing the aerodynamic drag the spacecraft experiences through modulation of the ballistic coefficient presents itself as the most feasible way to perform re-entry targeting. While extensive work has been done on density modeling and spacecraft drag estimation 8,9 , and there is a body of research focused on relative spacecraft maneuvering using differential drag [10][11][12] , very little research has been conducted on a de-orbit algorithm that utilizes solely aerodynamic drag to control the decay trajectory. Though the modulation of vehicle aerodynamics has been utilized since the Apollo missions 4 to help vehicles maintain a precomputed guidance during the re-entry trajectory, the use of solely aerodynamic drag for reentry targeting presents a much greater challenge and has never been done in practice before to the best of the authors' knowledge.…”
Section: Introductionmentioning
confidence: 99%
“…During the study it was observed that, in some cases, the trajectories generated using a constant density atmosphere influenced negatively in the final trajectory. This problem was already detected and studied by Bevilacqua et al (2016), where they proposed the use of a neural network for density forecast when generating the guidance trajectory.…”
Section: Discussionmentioning
confidence: 99%
“…Then, the first order time derivative of the Lyapunov function is as follows (Pérez & Bevilacqua, 2016):…”
Section: Lyapunov Controller Designmentioning
confidence: 99%
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