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

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this article, an artificial neural-network-based method is proposed for the rapid approximation of optimal perturbed long-duration impulsive transfers. The relationship between the optimal transfer velocity increments and the initial right ascension of the ascending node difference between the departure body and the rendezvous target is analyzed, and the result suggests that the perturbed long-duration impulsive transfers should be divided into three types. An efficient database generation method is developed. Three regression multilayer perceptrons (MLPs) are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained MLPs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all three types of transfers. Additional tests of the debris chains with total velocity increments of several thousand m/s show that the estimation results are very close to the optimized results, with a final estimation error of less than 10 m/s. [ABSTRACT FROM AUTHOR]