弹道学报2025，Vol.37Issue(4)：10-19,10.DOI:10.12115/ddxb.2025.10010
基于WOA-DNN的高超声速飞行器实时再入轨迹优化方法
Real-time Reentry Trajectory Optimization Method for Hypersonic Vehicles Based on WOA-DNN
摘要
Abstract
To address the real-time requirements for hypersonic vehicle reentry trajectory optimization,a real-time trajectory optimization method that integrates the whale optimization algorithm(WOA)with deep neural network(DNN)was proposed.Firstly,a reentry trajectory optimization model for a hypersonic vehicle was established.The original non-convex optimal control problem was transformed into a convex optimization problem for efficient solution via sequential second-order cone programming,generating an optimal trajectory dataset incorporating aerodynamic parameter uncertainties.Subsequently,a DNN was constructed,mapping the vehicle's state sequence to optimal bank-angle commands.To address the high sensitivity of DNN performance to hyperparameters such as initial weights and thresholds,the WOA was introduced to globally optimize these parameters,thereby significantly enhancing the prediction accuracy and generalization capability.In the final online planning stage,near-optimal control commands were generated in real time based on the actual flight states.Numerical simulations demonstrate that under nominal and aerodynamic uncertainty conditions,the proposed WOA-DNN optimization method rapidly generates feasible trajectories that satisfy terminal accuracy requirements,significantly enhances computational efficiency.This highlights comprehensive advantages of the method in terms of both precision and robustness for trajectory optimization.关键词
高超声速飞行器/再入轨迹优化/深度神经网络/鲸鱼优化算法/序列二阶凸规划Key words
hypersonic vehicle/reentry trajectory optimization/deep neural networks/whale optimization algorithm/sequential second-order cone programming分类
军事科技引用本文复制引用
代恩诚,蔡光斌,徐慧,魏昊,吕鑫,凡永华..基于WOA-DNN的高超声速飞行器实时再入轨迹优化方法[J].弹道学报,2025,37(4):10-19,10.基金项目
国家自然科学基金项目(62473374 （）
62403487) （）
