Decentralized model predictive control of swarms of spacecraft using sequential convex programming

Daniel Morgan, Soon-Jo Chung, Fred Y. Hadaegh

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)3835-3854
Number of pages20
JournalAdvances in the Astronautical Sciences
Volume148
StatePublished - Jan 1 2013
Event23rd AAS/AIAA Space Flight Mechanics Meeting, Spaceflight Mechanics 2013 - Kauai, HI, United States
Duration: Feb 10 2013Feb 14 2013

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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