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, -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 -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.