Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

In this paper, neural networks are trained to learn the optimal time, the initial costates, and the optimal control law of time-optimal low-thrust interplanetary trajectories. The aim is to overcome the difficult selection of first guess costates in indirect optimization, which limits their implemen...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.156413-156419
Main Authors: Li, Haiyang, Baoyin, Hexi, Topputo, Francesco
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological neural networks
Control theory
Global optimization
Indirect methods
initial costates
Interplanetary trajectories
low-thrust trajectory optimization
Neural networks
Optimal control
Performance assessment
Real-time systems
Time optimal control
Training
Trajectory
Voyager 1 spacecraft
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Summary:In this paper, neural networks are trained to learn the optimal time, the initial costates, and the optimal control law of time-optimal low-thrust interplanetary trajectories. The aim is to overcome the difficult selection of first guess costates in indirect optimization, which limits their implementation in global optimization and prevents on-board applications. After generating a dataset, three networks that predict the optimal time, the initial costate, and the optimal control law are trained. A performance assessment shows that neural networks are able to predict the optimal time and initial costate accurately, especially a 100% success rate is achieved when neural networks are used to initialize the shooting function of indirtect methods. Moreover, learning the state-control pairs shows that neural networks can be utilized in real-time, on-board optimal control.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2946657