基于物理信息神经网络的卫星集群构形设计方法OA
Satellite Formation Configuration Design Using Physics-Informed Neural Networks
本文提出一种基于物理信息神经网络的卫星集群构形设计方法,旨在解决传统非线性规划方法在高维多约束构形设计问题中存在的计算复杂度高、对初始猜测依赖性强等问题.该方法以相对偏心率矢量与相对倾角矢量作为神经网络输出,将碰撞规避、通信范围约束等任务要求与构形优化目标转化为损失函数中的物理惩罚项,从而在无数据集条件下实现端到端的构形优化.仿真结果表明,该方法在不同规模的集群任务中均能稳定输出满足约束条件的构形解,具备良好的物理一致性、求解稳定性和工程应用潜力.
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.
巩浩;王继河;卫国宁;李威
中山大学,深圳 518107中山大学,深圳 518107上海霄元创新中心,上海 201100上海霄元创新中心,上海 201100
航空航天
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
satellite formationformation configuration designrelative orbital elementsphysics-informed neural networkdataset-free solution
《空间控制技术与应用（中英文）》 2026 (1)
48-54,7
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)
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