有控弹箭气动参数辨识技术  

Research on aerodynamic parameter identification technologyof controlled projectile

作　　者：康其庄 王康健 易文俊 段耀泽 夏悠然 KANG Qizhuang;WANG Kangjian;YI Wenjun;DUAN Yaoze;XIA Youran(Nanjing University of Science and Technology,Key Laboratory of Transient Physics,Nanjing 210000,China;Air Force Engineering University,School of Aeronautical Engineering,Xi’an 710000,China)

机构地区：[1]南京理工大学瞬态物理重点实验室,南京210000 [2]空军工程大学航空工程学院,西安710000

出　　处：《兵器装备工程学报》2024年第5期209-214,共6页Journal of Ordnance Equipment Engineering

摘　　要：快速准确获取气动参数是精确制导的必要前提。针对受限于模型构建精度,传统气动参数辨识方法对受力复杂的有控弹箭气动参数辨识困难、精度不足等问题,引入Elman递归神经网络,利用Elman神经网络强大的延时记忆和非线性拟合能力辨识气动参数,探究Elman神经网络应用于有控弹箭气动参数辨识的可行性,并与BP神经网络辨识结果进行了对比。仿真结果表明,Elman神经网络能较好地辨识出滑翔飞行阶段的气动参数,且辨识精度要高于BP神经网络。Fast and accurate acquisition of aerodynamic parameters is a necessary prerequisite for precision guidance.Limited by the accuracy of model construction,the traditional aerodynamic parameter identification method is not accurate enough to identify the controlled projectile with complex forces.Aiming at the difficulty of aerodynamic parameter identification of controlled projectile,this paper introduces Elman recurrent neural network,uses Elman neural network’s powerful delay memory and nonlinear fitting ability to identify aerodynamic parameters,explores the feasibility of applying Elman neural network to aerodynamic parameter identification of controlled projectile,and compares the identification results with BP neural network.The simulation results show that Elman neural network can identify the aerodynamic parameters of gliding flight stage well,and the identification accuracy is higher than BP neural network.

关 键 词：有控弹箭 参数辨识 ELMAN神经网络 数据插值 

分 类 号：TJ012.3[兵器科学与技术—兵器发射理论与技术] TJ013.2