临近空间动能拦截器神经反演姿态控制器设计

张涛; 李炯; 王华吉; 雷虎民; 叶继坤

doi:10.7527/S1000-6893.2018.21953

航空学报 >

2018 , Vol. 39 >Issue 8: 321953 - 321953

DOI: https://doi.org/10.7527/S1000-6893.2018.21953

电子电气工程与控制

临近空间动能拦截器神经反演姿态控制器设计

张涛 ,
李炯 ,
王华吉 ,
雷虎民 ,
叶继坤

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1. 空军工程大学研究生院, 西安 710051;
2. 空军工程大学防空反导学院, 西安 710051

收稿日期: 2017-12-20

修回日期: 2018-05-28

网络出版日期: 2018-05-28

基金资助

国家自然科学基金（61773398，61703421）

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Attitude control of near space kinetic kill vehicle based on neural network backtepping control

ZHANG Tao ,
LI Jiong ,
Wang Huaji ,
LEI Humin ,
YE Jikun

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1. Graduate College, Air Force Engineering University, Xi'an 710051, China;
2. Air and Missile Defense college, Air Force Engineering University, Xi'an 710051, China

Received date: 2017-12-20

Revised date: 2018-05-28

Online published: 2018-05-28

Supported by

National Natural Science Foundation of China (61773398,61703421)

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摘要

为满足临近空间动能拦截器姿态控制快速性、准确性和鲁棒性的要求，设计了一种自适应神经反演姿态控制器。首先，建立了姿控发动机侧喷干扰模型，并推导了包含质心漂移、参数摄动和外界干扰的三通道强耦合模型；其次，设计了自适应神经反演姿态控制器，为提高控制精度，采用径向基函数（RBF）神经网络对各个通道的不确定项进行估计和补偿，并基于最小学习参数的思想，将神经网络学习参数拟合为一个参数，提高了RBF计算效率，保证了估计的实时性。最后，采用伪速率（PSR）脉冲调制器将设计的连续控制律转化为脉冲控制律，实现了拦截器的变推力控制，并克服了脉冲脉宽调制（PWPF）调制器相位滞后问题。数字仿真表明，所设计的控制器收敛速度快，控制精度高，对强扰动具有鲁棒性。

关键词： RBF神经网络; 侧喷干扰模型; 临近空间动能拦截器; PSR脉冲调节器; 反演控制

本文引用格式

张涛 , 李炯 , 王华吉 , 雷虎民 , 叶继坤 . 临近空间动能拦截器神经反演姿态控制器设计[J]. 航空学报, 2018 , 39(8) : 321953 -321953 . DOI: 10.7527/S1000-6893.2018.21953

Abstract

To meet the requirement for fast, accurate and robust attitude control of near space kinetic kill vehicles, an adaptive neural network backstepping attitude controller is designed. First, a model for lateral jet interaction is established, and a three-channel strong coupling model including centroid drift, parameter perturbation and external disturbance is developed. Second, an adaptive neural backstepping attitude controller is designed. Then, to improve control precision, the RBF neural network is adopted to estimate and compensate the uncertainties in each channel, and the neural network learning parameter is fitted as a parameter based on the theory of minimum learning parameters to improve Radial Basis Function (RBF) calculation efficiency and estimation instantaneity. Finally, a PSeudo Rate (PSR) modulator is designed to convert the continuous control law into the pulse control law, so as to realize control of KKV digital variable thrust. The simulation shows that the controller has fast convergence speed, high control precision and robustness to strong disturbance.

Key words： Radial Basis Function (RBF) neural network; lateral jet interaction model; near space kinetic kill vehicle; PSR modulator; backstepping control

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