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Rapid generation of time-optimal rendezvous trajectory based on convex optimisation and DNN

Published online by Cambridge University Press:  09 January 2024

R.D. Zhang Open the ORCID record for R.D. Zhang [Opens in a new window] ,
W.W. Cai Open the ORCID record for W.W. Cai [Opens in a new window]  and
L.P. Yang Open the ORCID record for L.P. Yang [Opens in a new window]
R.D. Zhang
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
W.W. Cai*
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
L.P. Yang
Affiliation:
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China
*
Corresponding author: W.W. Cai; Email: tsaiweiwei@hotmail.com
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Abstract

The minimum flight time of spacecraft rendezvous is one of the fundamental indexes for mission design. This paper proposes a rapid trajectory planning method based on convex optimisation and deep neural network (DNN). The time-optimal trajectory planning problem is reconstructed into a double-layer optimisation framework, with the inner being a convex optimisation problem and the outer being a root-finding problem. The thrust properties corresponding to time-optimal control are analysed theoretically. A DNN-based rapid planning method (DNN-RPM) is put forward to improve computational efficiency, in which the trained DNN provides a high-quality initial guess for Newton’s method. The DNN-RPM is extended to search for the optimal entering angle of natural-motion circumnavigation orbit injection problem and the minimum reconfiguration time of spacecraft swarm. Numerical simulations show that the proposed method can improve the computational efficiency while ensuring the calculation accuracy.

Keywords

time-optimal trajectoryconvex optimisationdeep neural networknatural-motion circumnavigation injectionswarm reconfiguration
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 22
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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