Online identification method for morphing vehicles with time-varying aerodynamic parameters
Owing to environmental variations and shape changes during actual flight,the complex aerodynamic char-acteristics of morphing vehicles are time-varying and highly nonlinear.This paper proposes an online identification method based on a BP neural network model to obtain the time-varying aerodynamic parameters of morphing vehi-cles with high precision.First,a BP neural network model was established to approximate the aerodynamic model within a certain precision range based on the nonlinear relationship between input and output.Then,the neural network was trained online using the extended Kalman filter method with observed data from actual aerodynamic pa-rameter tests.The BP neural network model could quickly calculate and predict the aerodynamic parameters after real-time correction and obtaining the neural network parameters.This enabled the tracking of changes in the rapid-ly time-varying and nonlinear aerodynamic model.Finally,a mathematical simulation was conducted to identify the aerodynamic parameters of a morphing air vehicle during successive deformation/structure mutation.The results verified that the proposed method has a fast convergence speed and high accuracy,demonstrating its effectiveness in identifying the aerodynamic parameters of morphing vehicles.
万方数据
维普
