Abstract:

Classical model-based aerodynamic parameter methods cannot solve the key problems in launch vehicle flight， such as the difficulty of obtaining dynamic pressure and angle of attack online， and low computational efficiency. To address the above problems， this paper proposes an online aerodynamic parameter identification method for launch vehicles based on an “offline learning + online correction” framework. In the offline learning process， the Long Short-Term Memory neural network （LSTM） is used to learn and extract the time series features from velocity， position and apparent acceleration， and the trained network model is used to output the axial force coefficient， normal force coefficient gradient， wind direction angle and wind speed. In the online correction part， based on the output value of the network model， the Recursive Least Squares （RLS） is used to identify the error increment of the aerodynamic parameters online， and then the error increment is superimposed with the network output value to obtain the online identification value of the aerodynamic parameters. In the simulation verification， the wind field uncertainty is introduced offline to generate sufficient training data for the LSTM neural network， and then the aerodynamic parameters are corrected online in combination with the RLS. The simulation results show that compared with using the neural network model alone， the identification method of'offline learning + online correction'proposed in this paper can significantly improve the identification accuracy while maintaining high computational efficiency.

Key words: launch vehicle, aerodynamic parameter, long short-term memory neural network, recursive least squares, online identification, offline learning