Decentralized Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming
- Creators
- Morgan, Daniel
-
Chung, Soon-Jo
- Hadaegh, Fred Y.
Abstract
This paper presents a decentralized, model predictive control algorithm for the reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In our prior work, sequential convex programming has been used to determine collision-free, fuel-efficient trajectories for the reconfiguration of spacecraft swarms. This paper uses a model predictive control approach to implement the sequential convex programming algorithm in real-time. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the convex optimizations, which reduces the run time of the algorithm.
Additional Information
© 2013 American Institute of Aeronautics and Astronautics. This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. © California Institute of Technology. This work was supported by a NASA Office of the Chief Technologists Space Technology Research Fellowship. Government sponsorship acknowledged.Attached Files
Accepted Version - Morgan_AAS_13_439.pdf
Files
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Additional details
- Eprint ID
- 73120
- Resolver ID
- CaltechAUTHORS:20161222-070302558
- NASA
- Created
-
2016-12-22Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Caltech groups
- GALCIT
- Series Name
- Advances in the Astronautical Sciences
- Series Volume or Issue Number
- 148