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Published November 2014 | public
Journal Article

Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming

Abstract

This paper presents a decentralized, model predictive control algorithm for the optimal guidance and reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In previous work, J_2-invariant orbits have been found to provide collision-free motion for hundreds of orbits in a low Earth orbit. This paper develops real-time optimal control algorithms for the swarm reconfiguration that involve transferring from one J_2-invariant orbit to another while avoiding collisions and minimizing fuel. The proposed model predictive control-sequential convex programming algorithm uses sequential convex programming to solve a series of approximate path planning problems until the solution converges. 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. Multiple time steps, time-varying collision constraints, and communication requirements are developed to guarantee stability, feasibility, and robustness of the model predictive control-sequential convex programming algorithm.

Additional Information

© 2013 American Institute of Aeronautics and Astronautics, Inc. This work was supported by a NASA Office of the Chief Technologist Space Technology Research Fellowship. Government sponsorship is acknowledged. This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the NASA.

Additional details

Created:
August 20, 2023
Modified:
October 24, 2023