Fast Evaluation of Low-Thrust Transfers via Multilayer Perceptions

The design of low-thrust-based multitarget interplanetary missions requires a method to quickly and accurately evaluate the low-thrust transfer between any two visiting targets. Complete evaluation of the low-thrust transfer includes not only the estimation of the optimal fuel consumption but also the judgment of transfer feasibility. In this Paper, a learning-based method is proposed for quickly evaluating low-thrust transfer. An efficient database-generation method for obtaining both the infeasible and optimal transfers is developed. A classification multilayer perception and a regression multilayer perception are trained based on the infeasible and optimal transfers to judge the transfer feasibility and estimate the optimal fuel consumption, respectively. The simulation results show that the well-trained multilayer perceptions are capable of quickly determining the transfer feasibility with an accuracy of greater than 98.3% and approximating the optimal transfer fuel consumption with a relative estimation error of less than 0.37%. The tests on two asteroid chains further show the superiority of the multilayer-perception-based method for application to the design of low-thrust-based multitarget interplanetary missions.