Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming

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, J2-invariant orbits have been found to provide collision-free motion f...

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Bibliographic Details
Published in:Journal of guidance, control, and dynamics control, and dynamics, 2014-11, Vol.37 (6), p.1725-1740
Main Authors: Morgan, Daniel, Chung, Soon-Jo, Hadaegh, Fred Y.
Format: Article
Language:English
Subjects:
Aerospace engineering
Algorithms
Collision avoidance
Computational geometry
Control algorithms
Control stability
Control theory
Convex analysis
Convexity
Copyright
Invariants
Low earth orbits
Mathematical models
Mathematical programming
Optimization
Orbits
Predictive control
Programming
Reconfiguration
Run time (computers)
Spacecraft
Spacecraft guidance
Time optimal control
Trajectory optimization
Trajectory planning
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Summary: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, J2-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 J2-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.
ISSN:0731-5090
1533-3884
DOI:10.2514/1.G000218