平台式导引头隔离度模型在线辨识方法

doi:10.12305/j.issn.1001-506X.2024.11.01

摘要/Abstract

摘要：

针对平台导引头隔离度模型在线辨识问题, 提出一种改进卷积神经网络的导引头隔离度模型辨识方法, 实现对不同干扰力矩以及天线罩误差等干扰参数影响下产生的隔离度模型高效辨识。首先, 建立平台导引头模型, 推导出隔离度传递函数, 并搭建基于隔离度寄生回路的制导回路平台, 获取弹体扰动下的视线角速率信息作为训练和测试数据。然后, 利用卷积神经网络对视线角速率信号进行特征提取和特征降维。最后, 经分类输出模型诊断结果。仿真结果表明，所提辨识方法对隔离度模型识别正确率能够达到99.7%，相较于传统方法提高了模型辨识准确率和快速在线辨识能力，具有较好的工程应用前景。

关键词: 平台导引头, 隔离度, 卷积神经网络, 模型辨识

Abstract:

To solve the problem of online identification of platform seeker disturbance rejection rate models, an improved convolutional neural network based seeker disturbance rejection rate model identification method is proposed to realize efficient identification of the model generated by different torques and radome errors. Firstly, the seeker model of the platform is established, the disturbance rejection rate transfer function is derived, the guidance loop platform based on the parasitic loop is built, and the line of sight angle rate information under the disturbance of the missile body is obtained as the training and test data. Then, convolutional neural network is used to extract and reduce the feature dimension of the line of sight angle rate signal. Finally, the model diagnosis results are output by classification. The simulation results show that the proposed identification method of disturbance rejection rate model recognition accuracy can reach 99.7%, which improves the identification accuracy and fast online identification ability compared with traditional methods, and has a good engineering application prospect.

Key words: platform seeker, disturbance rejection rate (DRR), convolution neural network (CNN), model identification

中图分类号:

TJ765.3

肖博文, 马泽远, 鲁天宇, 夏群利. 平台式导引头隔离度模型在线辨识方法[J]. 系统工程与电子技术, 2024, 46(11): 3595-3604.

Bowen XIAO, Zeyuan MA, Tianyu LU, Qunli XIA. On-line identification method for models of platform seeker disturbance rejection rate[J]. Systems Engineering and Electronics, 2024, 46(11): 3595-3604.

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参数	数值
跟踪回路增益k₁	12
稳定回路增益k₂	24
电感L/H	0.003 2
电阻R/Ω	1
力矩系数K_T/(N·m/A)	0.22
电机转动惯量J/(kg·m²)	0.002 195
反电动势系数K_E/(V/rad·s)	0.234 4

干扰参数	传递函数
弹簧力矩+阻尼力矩+R_dom	$\frac{R_{\mathrm{dom}} K_{1} K_{2} /\left(1-R_{\mathrm{dom}}\right)+K_{\omega}+K_{n} s}{\left(K_{n}+K_{2}\right) s+K_{\omega}+K_{1} K_{2}}$
弹簧力矩	$\frac{K_{\omega}}{K_{2} s+K_{\omega}+K_{1} K_{2}}$
阻尼力矩	$\frac{K_{n} s}{\left(K_{n}+K_{2}\right) s+K_{1} K_{2}}$
天线罩误差R_dom	$\frac{R_{\mathrm{dom}} K_{1} /\left(1-R_{\mathrm{dom}}\right)}{s+K_{1}}$
弹簧力矩+阻尼力矩	$\frac{K_{\omega}+K_{n} s}{\left(K_{n}+K_{2}\right) s+K_{\omega}+K_{1} K_{2}}$
弹簧力矩+R_dom	$\frac{R_{\mathrm{dom}} K_{1} K_{2} /\left(1-R_{\mathrm{dom}}\right)+K_{\omega}}{K_{2} s+K_{\omega}+K_{1} K_{2}}$
阻尼力矩+R_dom	$\frac{R_{\mathrm{dom}} K_{1} K_{2} /\left(1-R_{\mathrm{dom}}\right)+K_{n} s}{\left(K_{n}+K_{2}\right) s+K_{1} K_{2}}$

网络层	各层结构参数
输入层	1×800
EMSCNN层	卷积核尺寸(6、12), 扩展率(1、2、3)、数量(5、5)
池化层	2
BiLSTM层	15
全连接层	192
输出层	7

算法	准确率/%	训练时长/s
本文模型	99.7	412
CNN	98.9	457
DA	97.0	503
LSTM	94.6	486

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