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

Abstract

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

CLC Number:

TJ765.3

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.

Figures/Tables 18

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References 33

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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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On-line identification method for models of platform seeker disturbance rejection rate

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