基于BP神经网络的弹射座椅姿态控制律设计

王敏; 吴威涛; 封锋; 陈军; 吴明雨

doi:10.7638/kqdlxxb-2023.0143

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基于BP神经网络的弹射座椅姿态控制律设计

研究论文 | 更新时间：2026-07-27

- 基于BP神经网络的弹射座椅姿态控制律设计
- An attitude control scheme for ejection seats based on BP neural network
- 空气动力学学报 2024年42卷第11期页码：86-99
- 作者机构：
  
  1. 南京理工大学机械工程学院,南京,210094
  2. 南京理工大学瞬态物理重点实验室,南京,210094
- 作者简介：
  
  王敏（1998—），男，江西赣州人，硕士研究生，研究方向：空气动力学，飞行控制. E-mail：wangminwm@njust.edu.cn
  吴明雨*，博士研究生，研究方向：空气动力学，制导和机器学习. E-mail：wmyy@njust.edu.cn
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0143
  中图分类号： V244.21+2
- 纸质出版：2024
- 稿件说明：
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王敏, 吴威涛, 封锋, 等. 基于BP神经网络的弹射座椅姿态控制律设计[J]. 空气动力学学报, 2024, 42(11): 86−99.

WANG Min, WU Weitao, FENG Feng, et al. An attitude control scheme for ejection seats based on BP neural network[J]. 2024, 42(11): 86-99.
王敏, 吴威涛, 封锋, 等. 基于BP神经网络的弹射座椅姿态控制律设计[J]. 空气动力学学报, 2024, 42(11): 86−99. DOI： 10.7638/kqdlxxb-2023.0143

WANG Min, WU Weitao, FENG Feng, et al. An attitude control scheme for ejection seats based on BP neural network[J]. 2024, 42(11): 86-99. DOI： 10.7638/kqdlxxb-2023.0143.

摘要

为提升第四代弹射座椅低空不利姿态下的救生性能，本文建立了弹射座椅在自由飞阶段的动力学模型和中低速弹射状态的双通道姿态控制模型，提出了一种基于反向传播（back-propogation

BP）神经网络的PID（proportion integration differentiation）控制器的弹射座椅姿态控制方案，以实现对弹射座椅俯仰和横滚姿态的控制。该方案利用遗传算法设计了弹射座椅姿态控制参数寻优模型，获得部分弹射状态对应的最优PID控制参数映射数据集；基于此数据集，采用BP神经网络建立预测模型，直接预测弹射座椅在中低速弹射状态下所对应的姿态控制PID参数；随后，PID控制器根据这些参数输出燃气舵推力矢量喷管的两组舵偏角数值，燃气舵舵片偏转产生的升力和阻力作用于弹射座椅动力学模型，从而调整弹射座椅在自由飞阶段的姿态。为验证控制效果，采用MATLAB/Simulink仿真软件对所提控制方案在不同初始速度、俯仰角以及滚转角弹射状态下的表现进行仿真测试，通过观察弹射座椅在自由飞阶段的姿态角及角速度变化情况，发现弹射座椅的俯仰和滚转姿态均能在火箭包工作时间内调整回正。结果表明，本文设计的基于BP神经网络的PID控制器可以实现中低速弹射状态下弹射座椅的姿态控制。

Abstract

In order to improve the life-saving performance of the fourth-generation ejection seat under unfavorable conditions at low altitudes

we developed a dynamic model for free flight of the ejection seat and a dual-channel attitude control model for the ejection seat with low-to-medium ejection speeds. An attitude control scheme based on the above models using back-propagation (BP) neural network proportion integration differentiation (PID) controller was further proposed to control the pitching and rolling attitudes of the ejection seat. The proposed scheme uses a genetic algorithm to design a parameter optimization model for the ejection seat attitude control

which generates a set of optimal PID control parameters corresponding to different ejection states. Based on this dataset

a BP neural network model was developed and trained to directly predict the PID parameters for the attitude control of the ejection seat with low-to-medium ejection speeds. Finally

the PID controller outputs two sets of rudder deflection angles of the gas rudder thrust-vector nozzle. The lift and drag forces generated by the deflection of the gas rudder act on the ejection seat’s dynamic model

thereby adjusting the attitude of the ejection seat during the free-flight phase. The control scheme

validated by MATLAB/Simulink with different initial ejecting speeds

pitch angles

and roll angles

successfully adjusted the pitching and rolling attitudes of the ejection seat to normal values during the free flight within the rocket package's working time

demonstrating the promising application prospects of the proposed attitude control scheme for ejection seats with low-to-medium ejection speeds.

关键词

Keywords

references

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