[1]章? 钱,李士勇.一种新型自适应RBF神经网络滑模制导律[J].智能系统学报,2009,4(4):339-344.
　ZHANG Qian,LI Shi-yong.A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets[J].CAAI Transactions on Intelligent Systems,2009,4(4):339-344.
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一种新型自适应RBF神经网络滑模制导律

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 4 期数: 2009年第4期 页码: 339-344 栏目: 学术论文—机器学习 出版日期: 2009-08-25
Title:
A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets
文章编号:
1673-4785(2009)04-0339-06
作者:
章? 钱，李士勇
哈尔滨工业大学航天学院，黑龙江哈尔滨150001
Author(s):
ZHANG Qian, LI Shi-yong
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
关键词:
自适应控制; RBF神经网络; 导弹拦截; 滑模控制; 比例导引律
Keywords:
adaptive control;  RBFNN;  missile intercept;  sliding model control;  proportional navigation
分类号:
TJ765.3
文献标志码:
A
摘要:
针对导弹拦截问题，提出一种自适应RBF神经网络滑模制导律.首先根据准平行接近原理和变结构控制理论设计滑模面，然后将滑模面作为RBF神经网络的输入变量，输出量即为导弹的加速度.为了使得导弹系统能够到达滑模面，采用自适应算法实时在线调整RBF神经网络的连接权值.该导引律将目标机动视为干扰量，在拦截过程中不需要测量目标加速度,因此该导引律对目标机动具有较强的鲁棒性.在执行上，只用到了视线角速率，因而实现简单.仿真结果表明，所提出的导引律和比例导引相比在脱靶量、拦截时间等方面有了很大的提高.
Abstract:
A new adaptive radial basis function neural network (RBFNN) sliding mode guidance law was proposed for intercepting maneuvering targets. First of all, we designed a slidingsurface using a quasiparallel approach principle and variable structure control theory. We then used the sliding surface to input variables to the RBF neural network. In this case, the output was missile acceleration. In order to place the missile system on the sliding surface, we employed an adaptive algorithm that adjusts in realtime the connection weights of the RBF neural network. The missile acceleration in a given direction was determined by considering the target’s acceleration as a disturbance, and thus it was not necessary to measure the target’s acceleration directly. Therefore, this guidance law has strong robustness to target maneuvering. The new guidance law, which utilizes lineofsight (LOS) measurement only, is simple to implement. Numerical simulations showed that the proposed guidance law yields better performance than proportional navigation.
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备注/Memo

收稿日期：2009-04-15.
基金项目：国家自然科学基金资助项目（60773065）.
通信作者：章 钱.E-mail:qz50306@163.com
作者简介：
章 钱，男，1984年生，硕士研究生，主要研究方向为导航制导与控制、智能控制.
李士勇，男，1943年生，教授、博士生导师，国家模糊控制技术生产力促进中心专业组专家，中国自动化学会智能自动化专业委员会委员，《计算机测量与控制》杂志编委，哈尔滨工业大学学术委员会控制科学与工程分评委会委员.主要研究方向为模糊控制、智能控制、智能优化算法.主持和参加了国家自然科学基金、“973”项目等10项科研项目.获国家星火奖三等奖1项、获部级二等奖2项，三、四等奖共2项.近五年来，在国内外发表学术论文120余篇，其中多篇被SCI、EI和ISTP检索.

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