A computationally efficient model predictive control scheme for space debris rendezvous

Alexander Korsfeldt Larsén, Yutao Chen, Mattia Bruschetta, Ruggero Carli, Angelo Cenedese, Damiano Varagnolo, Leonard Felicetti

Research output: Contribution to journalConference articleAcademicpeer-review

1 Citation (Scopus)
32 Downloads (Pure)


We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for real-time non-linear model predictive control (MPC) called MATMPC. This tool computes the MPC rendezvous maneuvering solution in a numerically efficient way, and this allows to greatly extend the prediction horizon length. This implies that the overall MPC scheme can compute solutions that account for the long time-scales that usually characterize the low-thrust rendezvous maneuvers. The so-developed controller is then tested in a realistic scenario that includes all the near-Earth environmental disturbances. We thus show, through numerical simulations, that this MPC method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrolled object, plus evaluate performance indexes such as mission duration, fuel consumption, and robustness against sensor and process noises.
Original languageEnglish
Pages (from-to)103-110
Number of pages8
Issue number12
Publication statusPublished - Oct 2019


  • Constrained optimization problem
  • Low-thrust maneuvers
  • Non linear model predictive control
  • Non-cooperative rendezvous
  • Real-time control
  • Space debris removal
  • Non Linear Model Predictive Control
  • Real-time Control
  • Non-cooperative Rendezvous
  • Constrained Optimization Problem
  • Space Debris Removal
  • Low-Thrust Maneuvers


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