Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep Neural Networks

2019/02/11 by Yuehe Zhu, Yue-he Zhu, Zhu, Yue-he +3 · 1 citation
Computer Science · Engineering · Mathematics · #Algorithm #Approximation error #Artificial intelligence #Artificial neural network #Computer science #Control (management) #Control theory (sociology) #Duration (music) #FOS: Mathematics #Mathematical optimization #Mathematics #Node (physics) #Optimization and Control (math.OC) #Physics #Regression #Rendezvous #Spacecraft Dynamics and Control #Statistics #Target Tracking and Data Fusion in Sensor Networks #Transfer (computing) #Underwater Vehicles and Communication Systems #math.OC

paper · pdf · doi:10.48550/arxiv.1902.03741

published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:1902.03738

openalex publication_date 2019/02/11 · arxiv created 2019/02/25 · arxiv updated 2019/02/26 · openalex created_date 2019/03/02 · openalex updated_date 2026/07/28

Abstract

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this paper, a deep neural network (DNN)-based method is proposed for quickly approximating optimal perturbed long-duration impulsive transfers. This kind of transfer is divided into three types according to the variation trend of the right ascension of the ascending node (RAAN) difference between the departure body and the rendezvous target. An efficient database generation method combined with a reliable optimization approach is developed. Three regression DNNs are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained DNNs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all the three types of transfers. The tests on the debris chains with the total velocity increments of several thousand m/s show that the estimated results can be very close to the optimized ones with a final estimation error of less than 10 m/s.

Citations

• Deep Learning in Neural Networks: An Overview
• An overview of gradient descent optimization algorithms
• Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
• TensorFlow: A system for large-scale machine learning
• Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks

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