基于BP神经网络的降落伞气动力参数辨识  

Aerodynamic Parameter Estimation of Parachute Based on BP Neural Network

作　　者：昌飞 贾贺[1,2] CHANG Fei;JIA He(Beijing Institute of Space Mechanics&Electricity,Beijing 100094,China;College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]北京空间机电研究所,北京100094 [2]南京航空航天大学航空学院,南京210016

出　　处：《航天返回与遥感》2024年第2期19-28,共10页Spacecraft Recovery & Remote Sensing

摘　　要：针对降落伞回收系统动力学仿真中的气动力参数不能直接测量的问题,建立了降落伞系统稳定下降阶段的六自由度动力学方程和运动学方程,确定了气动力的形式以及待辨识参数。在此基础上采用了两种基于反向传播(Back Propagation,BP)神经网络的气动力参数辨识方案,使用飞行状态数据训练神经网络直至收敛,得到待辨识的气动力参数模型。通过仿真算例验证了两种辨识方案的有效性和辨识模型的正确性,分别得到气动力参数辨识结果,并计算了性能评价指标。根据仿真结果从收敛速度、辨识精度等方面分析了两种辨识方案的效果,结果显示:两种辨识方案预测结果与预期结果均吻合较好,但是双BP神经网络方法更具有优势。结果证明BP神经网络方法对未来工程中的空投试验数据辨识具有潜在应用价值。In response to the problem of the unmeasurable aerodynamic parameters in the dynamic simulation of parachute recovery systems,a six-degree-of-freedom dynamics equation and kinematic equation were established for the stable descent phase of the parachute system.The form of aerodynamics and the parameters to be identified were defined.In the foundation above,two aerodynamic parameter identification schemes based on BP neural networks were employed.These schemes involved training the neural networks with flight state data until convergence,resulting in the identification of the aerodynamic parameter model to be discerned.The effectiveness and accuracy of the two identification schemes are verified through simulation examples.The identification results of aerodynamic parameters are obtained separately,and performance evaluation metrics are calculated.The simulation results are analyzed in terms of convergence speed,identification accuracy,and other aspects,indicating that both identification schemes exhibited good agreement between predicted results and expected results.However,the double BP neural network method demonstrated superior performance.The findings of this study demonstrate the potential applicability of BP neural network methods in the identification of experimental data in future engineering applications.

关 键 词：降落伞 稳定下降阶段 参数辨识 BP神经网络 航天返回 

分 类 号：V445[航空宇航科学与技术—飞行器设计] TP183[自动化与计算机技术—控制理论与控制工程]