Machine Learning Method of Estimation for Fuel Consumption of Low-Thrust Transfers

Abstract

It is often necessary to solve complex global optimization problems in the preliminary deep space mission design. The exact solution to the design and optimization of low-thrust trajectory is more difficult and time-consuming, because of the limitation of calculation ability and time,it's impossible to solve each low-thrust problem accurately using numerical methods in the global optimization process. In this paper,we propose a machine learning method to estimate the fuel consumption for fueloptimal low-thrust transfer. The results show the performance is better compared with the Lambert method which is commonly used at present. Different features are used for machine learning,and the major differences are different orbit description and whether Lambert estimation result is considered. The feature with equinoctial orbit elements and Lambert estimation is the best feature. It can provide reference for future orbit design of deep space exploration mission.

Keywords

low-thrust / fuel optimal / fast estimation / machine learning