MS thesis abstract - Ferguson, Philip
| Author: | Ferguson, Philip |
| Degree: | Masters of Science |
| SERC #: | 3-03 |
| File type: | PDF, 2975 kB |
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Distributed Estimation and Control Technologies for Formation Flying Spacecraft
Many future space missions, such as space-based radar, earth mapping, and interferometry, will require formation flying of multiple spacecraft to achieve their very advanced science objectives. While formation flying offers many performance and operational advantages, there are several challenges that must be addressed, including navigation, control, autonomy, distributed data management, efficient inter-vehicle communication, and robustness. One of the key issues with formation flying of large fleets is selecting the overall system architecture, because it drives the distribution of the various algorithms and the extent to which data must be transmitted.
These challenges are particularly evident with the relative navigation. While carrier-phase differential GPS can be used as a highly accurate sensor for LEO formations, it is not sufficient as the sole sensor for missions beyond LEO. If local ranges and range rates are used to augment or replace the GPS measurements, precise estimation can continue into MEO and beyond. However these new measurements complicate the estimator decentralization by coupling the vehicles' state estimates.
This thesis explores solutions to many of these challenges within the context of the Orion microsatellite formation flying mission. It also presents the Formation Flying Information Technology testbed, developed to evaluate the communication and computational requirements associated with various system architectures when using augmented GPS. Several architectures and their associated estimation algorithms are also analyzed and compared in terms of performance, computation, and communication requirements. This analysis clearly shows that the decentralized reduced-order filters provide near optimal estimation without excessive communication or computation requirements. Embedding these reduced-order estimators within the hierarchic architecture presented should also permit scaling of the relative navigation to very large fleets.