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

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