Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 4: 90 - 101

DOI: https://doi.org/10.12141/j.issn.1000-565X.230781

Mechanical Engineering

Optimal Selection of UAV Launch Parameters Based on SSA-BP Neural Network

  • JIA Huayu ,
  • ZHENG Huilong ,
  • ZHOU Hong ,
  • ZHANG Qian
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  • 1.School of Aeronautics and Astronautics，University of Chinese Academy of Sciences，Beijing 100049，China
    2.Institute of Engineering Thermophysics，Chinese Academy of Sciences，Beijing 100190，China
贾华宇（1991—），男，博士，工程师，主要从事巡飞无人机总体设计、气动优化、发射分系统设计优化等研究。E-mail： jiahuayu@iet.cn

Received date: 2023-12-21

  Online published: 2024-07-20

Supported by

the Key Deployment Project of the Chinese Academy of Sciences(KGFZD-145-22-02);the National Key Laboratory Fund for Light Turbine Power(E31H890206)

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Abstract

Rocket assisted zero length launch is an important form of UAV launch. The selection of launch parameters such as launch angle, booster angle, and booster thrust directly affects the success or failure of UAV launch mission. In the design phase of unmanned aerial vehicle rocket assisted zero length launch, key parameters such as launch angle, booster angle, and booster thrust are selected based on engineering experience. However, this approach faces several challenges, including long iteration cycles for optimal parameter selection, poor design interactivity, and the potential risk of causing instability in the UAV’s flight posture. This study focused on a specific UAV and conducts dynamic and kinematic modeling of its launch phase, constructing a six-degree-of-freedom nonlinear model. A UAV launch trajectory parameterization simulation software was developed using QT/C++ software, and the software’s effectiveness was verified through comparison with real UAV launch test data. At the same time, in order to solve the problem of autonomous optimization of launch parameters, sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA) optimization modules were introduced based on the backpropagation neural network (BP neural network) parameter prediction model. A UAV launch parameter optimization method based on SSA optimized BP neural network was proposed to eliminate the overfitting and local optimal effects of BP neural network in the parameter prediction process. The absolute error (MAE), average percentage error (MAPE), and root mean square error (RMSE) of the parameter prediction results were calculated to comprehensively evaluate the superiority of SSA-BP in predicting launch parameters, and the rationality of the launch parameter selection was verified through launch trajectory verification. The results indicate that the SSA-BP model has the highest prediction accuracy and robustness for launch parameters, and can provide a design basis for the autonomous selection of launch parameters in the engineering design stage of unmanned aerial vehicle launch subsystems.

Key words： UAV launch; sparrow search algorithm; BP neural network; parameter optimization; modeling and simulation

Cite this article

JIA Huayu , ZHENG Huilong , ZHOU Hong , ZHANG Qian . Optimal Selection of UAV Launch Parameters Based on SSA-BP Neural Network[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(4) : 90 -101 . DOI: 10.12141/j.issn.1000-565X.230781

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