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Research Topic Description, including Problem Statement:
Satellite formation flying offers two significant benefits: 1) graceful degradation: if one satellite in the formation fails, the remaining satellites may be able to continue collecting mission data, and 2) potential performance increases: distributed aperture sensing technology theoretically provides transformational improvements. However, in order to realize these benefits, one must be able to maintain the formation in the presence of dynamic perturbations. Classical control approaches involve targeting desired orbital elements or relative orbital elements, but these approaches assume that one knows what the desired elements are. If the orbital dynamics were perfectly known, this would not be an issue. However, there are still uncertainties in the gravity field and atmosphere, and more importantly, minor differences in satellite makeup and orientation; combined, these uncertainties introduce inefficiencies into formation control. Furthermore, if one satellite is lost from the formation, how does the formation optimally reconfigure itself? It is hypothesized that a better approach to achieving and maintaining satellite formations can be found. The goal of this research project is to review the state-of-the-art literature and explore methods to improve satellite formation control in the presence of dynamic model errors.
Example Approaches:
Instead of viewing formations in terms of orbital elements or element differences, one could cast the problem in terms of controlling relative orbital element rate differences. For example, if two satellites were flying in the same orbital plane separated by several degrees of mean anomaly, one would expect small perturbations, due to longitude-dependent gravity and drag effects, to slowly impact the mean anomaly difference. One could execute small maneuvers to maintain the target mean anomaly difference each time the separation distance changes beyond a tolerance value. Alternatively, one could change the reference semimajor axis by a small amount to offset the unmodeled dynamic perturbation. This second approach would require far fewer maneuvers. Less elegantly, one could also approach the problem from a machine-learning point of view.
Relevance to the Intelligence Community:
Satellite formation flying offers increased resilience and potentially improved intelligence, surveillance, and reconnaissance (ISR) data collection capabilities.
Key Words: Control Theory, Satellite Formation Flying, Satellites, Machine Learning, Space
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