一种基于RBF神经网络增益调节的三维鲁棒导引律设计

doi:10.3969/j.issn.0253-2778.2015.04.004

中国科学技术大学学报 ›› 2015, Vol. 45 ›› Issue (4): 280-285.DOI: 10.3969/j.issn.0253-2778.2015.04.004

• 论著 • 上一篇

一种基于RBF神经网络增益调节的三维鲁棒导引律设计

陈勃，杨开红，季海波

1.中国科学技术大学信息科学技术学院自动化系,安徽合肥 230027；2.海军蚌埠士官学校一系,安徽蚌埠 233012

收稿日期:2014-09-30 修回日期:2015-03-06 接受日期:2015-03-06 出版日期:2015-03-06 发布日期:2015-03-06
通讯作者: 季海波
作者简介:陈勃，男，1984年生，硕士研究生，研究方向：导航、制导与控制，E-mail：447001023@qq.com
基金资助:
国家自然科学基金(61273090)资助.

A three dimensional robust guidance law design based on RBF neural network gain adjustment

CHEN Bo, YANG Kaihong, JI Haibo

1. Department of Automation, University of Science and Technology of China, Hefei 230027, China; 2. Department No.1, Bengbu Naval Petty Officer Academy, Bengbu 233012, China

Received:2014-09-30 Revised:2015-03-06 Accepted:2015-03-06 Online:2015-03-06 Published:2015-03-06

摘要/Abstract

摘要： 针对导弹-目标相对运动三维非线性模型，采用满足输入-状态稳定性理论的非线性导弹导引律，利用径向基函数(radial basis function，RBF)神经网络动态调节自主学习能力，得到一种能根据视线角速度变化情况动态调节非线性导引律增益的控制律，可以避免因增益固定而目标机动性大引起脱靶量增大的情况. 该导引律在目标做多种机动时也能对其进行跟踪和有效拦截. 仿真结果表明，该控制律具有良好的自适应能力且便于实现.

关键词: 导引律, 径向基函数神经网络(RBFNN), 输入-状态稳定性

Abstract: By adopting the three dimensional nonlinear model for the relative motion of missiles and targets，a scheme of guidance law was presented. The theoretical basis of the guidance law includes input-to-state stability (ISS) as well as the dynamic adjustment and self-study ability of the radial basis function (RBF) neural network. The control law is capable of dynamically adjusting the gain of nonlinear guidance law with the angular rate change of LOS (line of sight). The guidance law can avoid the undershoot augment caused by gain fixation and large-scale target-maneuvering, and also effectively trace as well as intercept the target making a variety of maneuvers. The numerical simulation results demonstrate the adaptivity and easy implementation of the control law.

Key words: guidance law, radial basis function neural network(RBFNN), input-to-state stability

中图分类号:

TP13

陈勃，杨开红，季海波. 一种基于RBF神经网络增益调节的三维鲁棒导引律设计[J]. 中国科学技术大学学报, 2015, 45(4): 280-285.

CHEN Bo, YANG Kaihong, JI Haibo. A three dimensional robust guidance law design based on RBF neural network gain adjustment[J]. Journal of University of Science and Technology of China, 2015, 45(4): 280-285.

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一种基于RBF神经网络增益调节的三维鲁棒导引律设计

A three dimensional robust guidance law design based on RBF neural network gain adjustment

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