| 基于RBF网络增益自适应调节的滑模制导律 |
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| 引用本文： | 李士勇,章钱.基于RBF网络增益自适应调节的滑模制导律[J].测试技术学报,2009,23(6):471-476. |
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| 作者姓名： | 李士勇 章钱 |
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| 作者单位： | 哈尔滨工业大学,航天学院控制科学与工程系,黑龙江,哈尔滨,150001 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 针对被动寻的导弹拦截问题,在变结构控制理论的基础上,应用RBF神经网络控制,提出一种新型导引律.首先应用变结构控制理论建立滑模变结构制导律.然后应用RBF神经网络对制导律中变结构项的增益进行调节,克服了变结构制导律中变结构强度项不易确定的缺点,从而减弱变结构控制的抖振,提高了导弹拦截的精度.仿真结果表明,该导引律具有较强的自适应能力,具有实时性好及便于实现的优点;同传统的变结构制导律、比例导引律相比,在脱靶量、拦截时间等方面都有了显著的提高.
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| 关 键 词： | RBF神经网络 变结构 制导律 导弹拦截 |
Sliding Mode Guidance Based on RBFNN Adaptive Gain Adjustment |
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| LI Shiyong,ZHANG Qian.Sliding Mode Guidance Based on RBFNN Adaptive Gain Adjustment[J].Journal of Test and Measurement Techol,2009,23(6):471-476. |
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| Authors: | LI Shiyong ZHANG Qian |
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| Affiliation: | LI Shiyong,ZHANG Qian(Dept.of Control Science & Engineering,School of Aerospace,Harbin Institute of Technology,Harbin 150001,China) |
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| Abstract: | A new guidance law is proposed by using RBF neural network technology and variable structure control theory for the passive homing guidance.First of all,the sliding mode variable structure guidance law is derived from variable structure control theory,then we use RBF neural network to adjust the gain of the sliding model guidance.So it overcomes the disadvantage that the strength of variable structure item is not easy to be determined.Therefore it can reduce the chattering and improve the accuracy of missil... |
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| Keywords: | RBF neural network variable structure guidance law missile intercept |
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