航空学报2025，Vol.46Issue(9)：78-90,13.DOI:10.7527/S1000-6893.2024.30979
基于多源域融合代理模型的氢能无人机优化设计
Optimal design of hydrogen-powered UAV based on multi-source domain fusion surrogate model
李荣祖 1刘莉 1杨盾1
作者信息
- 1. 北京理工大学 宇航学院,北京 100081
- 折叠
摘要
Abstract
This paper addresses the optimization problem of the overall design stage of Hydrogen-powered Un-manned Aerial Vehicles(H-UAVs)in the context of heterogeneous multi-source domains.It explores how to effectively utilize the transfer learning technology to establish a surrogate model and optimize H-UAVs in the presence of hetero-geneous samples.To solve the problem of high cost of building a surrogate model due to heterogeneous samples dur-ing the evolution of hydrogen-powered UAVs,a framework for establishing a Multi-Source domain Fusion(DG-MSF)surrogate model is proposed based on Data Generation.The geodesic flow kernel method is used to map the hetero-geneous source and target domains to a high-dimensional space to determine the relationship between multi-source domains.The marginal distribution-based data generation method is used to effectively integrate source domain infor-mation.A multi-layer perceptron neural network is built as a surrogate model,and is trained and fine-tuned through pre-training and fine-tuning methods to achieve efficient prediction of performance of H-UAVs.Finally,the optimization design of the H-UAV is carried out.The analysis results show that the proposed method can effectively utilize multi-source domain data to improve the efficiency of model training and prediction accuracy and the overall performance of H-UAVs,providing powerful technical support for the development of H-UAVs.关键词
氢能电动无人机/迁移学习/代理模型/多目标优化/多源域/预训练-微调Key words
hydrogen-powered unmanned aerial vehicle(H-UAV)/transfer learning/surrogate model/multi-objective optimization/multi-source domain/pre-training fine-tuning分类
航空航天引用本文复制引用
李荣祖,刘莉,杨盾..基于多源域融合代理模型的氢能无人机优化设计[J].航空学报,2025,46(9):78-90,13.
