Multi-objective optimization of low-thrust propulsion systems for multi-target missions using ANNs

Viavattene, G. , Grustan-Gutierrez, E. and Ceriotti, M. (2022) Multi-objective optimization of low-thrust propulsion systems for multi-target missions using ANNs. Advances in Space Research, 70(8), pp. 2287-2301. (doi: 10.1016/j.asr.2022.07.039)

Abstract

Multi-target missions are an attractive solution to visit multiple bodies, increasing the scientific return and reducing the cost, compared to multiple missions to individual targets. Examples of multi-target missions are multiple active debris removals (MADR) and multiple near-Earth asteroids rendezvous (MNR) missions. MADR missions allow for the disposal of inactive satellites, preventing the build-up of space junk, while MNR missions allow to reduce the expenses of each asteroid observation. Since those missions are long and highly demanding in terms of energy, it is paramount to select the most convenient propulsion system so that the propellant mass and the duration of the mission are minimized. To this end, this paper proposes the use of a multi-objective optimization and artificial neural networks. The methodology is assessed by optimizing trajectories for MADR and MNR sequences with off-the-shelf thrusters. Multiple Pareto-optimal solutions can be identified depending on the propulsion system characteristics, enabling mission designers to trade-off the different options quickly and reliably.

• Item Type: Articles
• Additional Information: Giulia Viavattene gratefully acknowledges the support received for this research from the James Watt School of Engineering at the University of Glasgow for funding the research under the James Watt sponsorship program.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Grustan Gutierrez, Dr Enric and Viavattene, Giulia and Ceriotti, Dr Matteo
• Authors: Viavattene, G., Grustan-Gutierrez, E., and Ceriotti, M.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Advances in Space Research
• Publisher: Elsevier
• ISSN: 0273-1177
• ISSN (Online): 1879-1948
• Published Online: 22 July 2022
• Copyright Holders: Copyright © 2022 Published by Elsevier B.V. on behalf of COSPAR
• First Published: First published in Advances in Space Research 70(8): 2287-2301
• Publisher Policy: Reproduced under a Creative Commons License