Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks
This provides a fast and accurate evaluation tool for designing low-thrust-based multitarget interplanetary missions, though it appears incremental as it applies existing DNN techniques to a specific domain problem.
The paper tackled the problem of quickly evaluating low-thrust transfers for interplanetary missions by proposing a deep neural network-based method, achieving a correct rate of over 98% for feasibility judgment and a relative estimation error below 0.4% for fuel consumption.
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 deep neural network (DNN)-based method is proposed for quickly evaluating low-thrust transfer. An efficient database generation method is developed for obtaining both the infeasible and optimal transfers. A classification DNN and a regression DNN 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 DNNs are capable of quickly determining the transfer feasibility with a correct rate of greater than 98% and approximating the optimal transfer fuel consumption with a relative estimation error of less than 0.4%. The tests on two asteroid chains further show the superiority of the DNN-based method for application to the design of low-thrust-based multitarget interplanetary missions