| description abstract | To solve the aircraft dynamics modeling problem in the entire envelope range, this work proposes a closed-loop system identification method based on deep learning. A closed-loop flight test was designed, under the framework of the closed-loop flight test, the motion mode of the aircraft was fully stimulated by the input signal of the control rudder surface and the airspeed and position commands. The lateral and longitudinal aerodynamic coefficients were solved from the flight test data, and the black box relationship between the aerodynamic coefficients and their influencing factors was established based on the deep network technology. The aerodynamic coefficient black box model was combined with the dynamics and kinematic equations of the aircraft to form a deep network dynamic model of the aircraft, which belongs to a gray box dynamics model. The deep network can easily and uniformly process different batches of flight test data, thus combining the flight test data at different flight state points, and finally building a complete aerodynamic model within the entire envelope range. Three groups of flight tests were performed: the first group of tests was used for model set training, the second group of test data was used for the selection of the best model, and the third group of flight tests was used for model validation. The model verification was completed from two aspects: the prediction of the aerodynamic coefficient and the prediction of the flight state variables. The results show that the deep network model can complete high-precision modeling of aerodynamic coefficients; and the gray box dynamic model can complete the modeling of aircraft dynamics within the entire envelope, and can be used as a long-term, high-precision flight simulator. | |
