arXiv subjects

Ya-zhong Luo

Publications and source records attributed to Ya-zhong Luo.

3 recordsLinked to original sources

arXiv2021

Fast Optimization of Impulsive Perturbed Orbit Rendezvous with Finite Iterations

A novel fast multi-impulse optimization method for long-duration perturbed orbit rendezvous is proposed. First, based on the analytically estimated impulses, the terminal rendezvous deviation with precise dynamics model can be predicted. Then, an analytical correction to the impulses using the deviations of orbit elements can be calculated based on the analytical J2 perturbed dynamics equation of a circular orbit. The iteration process repeating prediction and correction is then designed to quickly obtain a precise solution and trajectory. The simulation results proved that the iteration method adapts well to the analytical dynamics and high-precision dynamics. The deviation could always converge within five iterations.

nlin.CD↗

arXiv2019

Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep 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 paper, a deep neural network (DNN)-based method is proposed for quickly approximating optimal perturbed long-duration impulsive transfers. This kind of transfer is divided into three types according to the variation trend of the right ascension of the ascending node (RAAN) difference between the departure body and the rendezvous target. An efficient database generation method combined with a reliable optimization approach is developed. Three regression DNNs are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained DNNs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all the three types of transfers. The tests on the debris chains with the total velocity increments of several thousand m/s show that the estimated results can be very close to the optimized ones with a final estimation error of less than 10 m/s.

math.OC↗

arXiv2019

Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks

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

cs.LG↗