On-board modeling of gravity fields of elongated asteroids using Hopfield neural networks

作     者：Yingjie Zhao Hongwei Yang Shuang Li Yirong Zhou 

作者机构：College of AstronauticsNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China 

出 版 物：《Astrodynamics》 (航天动力学（英文）)

年 卷 期：2023年第7卷第1期

页      面：101-114页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070401[理学-天体物理] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.12102177) the Natural Science Foundation of Jiangsu Province(No.BK20220130) 

主　　题：elongated asteroids simplified model Hopfield neural networks(HNNs) on-board learning gravity inversion 

摘      要：To rapidly model the gravity field near elongated asteroids,an intelligent inversion method using Hopfield neural networks(HNNs)is proposed to estimate on-orbit simplified model ***,based on a rotating mass dipole model,the gravitational field of asteroids is characterized using a few *** solve all the parameters of this simplified model,a stepped parameter estimation model is constructed based on different gravity field ***,to overcome linearization difficulties caused by the coupling of the parameters to be estimated and the system state,a dynamic parameter linearization technique is proposed such that all terms except the parameter terms are known or ***,the Lyapunov function of the HNNs is matched to the problem of minimizing parameter estimation *** values of the Lyapunov function areused as estimated *** proposed method is applied to natural elongated asteroids 216 Kleopatra,951 Gaspra,and 433 *** results indicate that this method can estimate the simplified model parameters rapidly,and that the estimated simplified model provides a good approximation of the gravity field of elongated asteroids.