关键词: 气动参数辨识方法, GRU网络模型, GN优化算法, 实测飞行数据, 网络参数

Abstract: This paper proposes a new aerodynamic parameter identification method that combines a gated recurrent unit (GRU) neural network model and the Gauss-Newton (GN) optimization algorithm, aiming to accurately identify the unknown aerodynamic parameters from longitudinal flight data generated by a self-developed small-sized unmanned aerial vehicle (UAV) and ATTAS aircraft. The GRU network model was designed and then trained to characterize the chosen aircraft systems’ dynamics while avoiding numerical inte-grals of the postulated dynamic models. The GN optimization algorithm incorporates the trained GRU network model to obtain high-confidence identification results by iteratively minimizing a cost function concerning the unknown aerodynamic parameters. The measured flight data includes non-stall flight data from the self-developed UAV and near-stall flight data from the ATTAS aircraft. The study results demonstrate that the GRU network model can maintain reliable prediction effects for the non-stall and near-stall flight data by adjusting the corresponding network parameters (e.g., the number of hidden layers, number of units in each hidden layer, dropout rate, time-step, and learning rate). In addition, the proposed parameter identification method’s effectiveness is con-firmed by comparing the identified values of the chosen aircraft’s aerodynamic parameters with the corresponding wind tunnel or reference values.

Key words: aerodynamic parameter identification method, GRU network model, GN optimization algorithm, measured flight data, network parameters