空间控制技术与应用（中英文）2026，Vol.52Issue(1)：48-54,7.DOI:10.3969/j.issn.1674-1579.2026.01.005
基于物理信息神经网络的卫星集群构形设计方法
Satellite Formation Configuration Design Using Physics-Informed Neural Networks
摘要
Abstract
A physics-informed-neural-network-based method for satellite formation configuration design is proposed,which overcomes the high computational complexity and dependence on initial guesses inherent to traditional nonlinear programming approaches.Formation parameters(relative eccentricity vector and relative inclination vector)are encoded as the neural network's outputs,while mission constraints(collision avoidance and communication-range limits)and the optimization objective(safety margin)are transformed into physics-based penalty terms in the loss function,enabling training without any dataset.Simulation experiments verify the physical consistency of the proposed method under complex constraints.关键词
卫星集群/构形设计/相对轨道要素/物理信息神经网络/无数据集求解Key words
satellite formation/formation configuration design/relative orbital elements/physics-informed neural network/dataset-free solution分类
航空航天引用本文复制引用
巩浩,王继河,卫国宁,李威..基于物理信息神经网络的卫星集群构形设计方法[J].空间控制技术与应用（中英文）,2026,52(1):48-54,7.基金项目
国家自然科学基金资助项目(52272408)和广东省基础与应用基础研究基金(2023B1515120018) National Natural Science Foundation of China(52272408)and Guangdong Basic and Applied Basic Research Foundation(2023B1515120018) （52272408）
