拦截机动目标的三维自适应神经网络制导律

doi:10.16182/j.issn1004731x.joss.19-0434

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (2): 453-460.doi: 10.16182/j.issn1004731x.joss.19-0434

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

拦截机动目标的三维自适应神经网络制导律

司玉洁, 熊华^*, 李喆

北京电子工程总体研究所,北京 100854

收稿日期:2019-08-22 修回日期:2020-05-17 出版日期:2021-02-18 发布日期:2021-02-20
通讯作者: 熊华(1987-),男,博士,高工,研究方向为制导控制。E-mail: xiong_hua_@126.com
作者简介:司玉洁(1989-),女,博士,工程师,研究方向为导航,制导与控制。E-mail: siyujiehit@126.com

Three-dimensional Adaptive Neural Network Guidance Law against Maneuvering Targets

Si Yujie, Xiong Hua^*, Li Zhe

Beijing Institute of Electronic System Engineering, Beijing 100854, China

Received:2019-08-22 Revised:2020-05-17 Online:2021-02-18 Published:2021-02-20

摘要/Abstract

摘要： 针对导弹拦截机动目标及导弹执行机构存在物理受限的问题,研究了三维非线性抗饱和制导律的设计问题,基于径向基函数神经网络法以及自适应法设计了抗干扰以及抗饱和的终端滑模制导律,该制导律是有界的并且能够保证系统状态一致最终有界,与传统抗饱和制导律相比具有收敛速度快和精度高的优势。进行了理论证明。给出数学仿真实验结果,验证了所设计制导律的有效性以及优越性。

关键词: 滑模控制, 抗饱和制导律, 神经网络算法, 自适应算法

Abstract: The problem of designing a three-dimensional nonlinear guidance law accounting for saturation nonlinearity is concentrated to attack maneuvering targets. To solve the physical constraints of missile actuators, an anti-disturbance and anti-saturation terminal sliding mode guidance law is provided based on radial basis functions neural networks and adaptive method. The guidance law is bounded and ensures that the system state is uniformly ultimately bounded. Compared with the traditional anti-saturation guidance law, it has the advantages of fast convergence speed and high precision. Numerical simulations are introduced to demonstrate the effectiveness and superiority of the designed composite guidance law in theory.

Key words: sliding mode control, anti-saturation guidance law, neural network method, adaptive algorithm

中图分类号:

TP391.9

司玉洁, 熊华, 李喆. 拦截机动目标的三维自适应神经网络制导律[J]. 系统仿真学报, 2021, 33(2): 453-460.

Si Yujie, Xiong Hua, Li Zhe. Three-dimensional Adaptive Neural Network Guidance Law against Maneuvering Targets[J]. Journal of System Simulation, 2021, 33(2): 453-460.

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拦截机动目标的三维自适应神经网络制导律

Three-dimensional Adaptive Neural Network Guidance Law against Maneuvering Targets

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