Compared with the forced oscillation test in a conventional wind tunnel test, the wind tunnel virtual flight test can achieve partially constrained dynamic motion of the model in the wind tunnel by manipulating the control surface deflection. In this paper, a new four-degree-of-freedom (4-DOF) wind tunnel virtual flight test device is established. Compared with the 3-DOF wind tunnel virtual flight test with rotation around the center of mass, the model adds vertical motion, enabling it to simulate free flight more realistically. In this paper, the nonlinear flight dynamics model of 4-DOF wind tunnel virtual flight test is established, the differences in dynamics characteristics between it and free flight are analyzed, and the identification model of aerodynamic derivatives based on 4-DOF wind tunnel virtual flight test is derived. To ensure the safety of the test, the longitudinal altitude control law as well as the sideslip and roll angle control laws in the lateral direction are designed. Finally, a set of aerodynamic parameter identification methods based on a 4-DOF wind tunnel virtual flight test is formed. The output error method is used to identify the aerodynamic derivatives of the test model, and the accuracy of the identification results is higher than that of the 3-DOF wind tunnel virtual flight test.

The wind tunnel virtual flight test realizes the dynamic semi-free flight of the model in the wind tunnel through the deflections of the control surface and uses the test data to identify the aerodynamic derivatives. The difference in dynamics between the wind tunnel virtual flight and the free flight leads to discrepancies between the identification and theoretical results. To solve the problems, a step-by-step identification and correction method for aerodynamic derivatives is established based on the difference between the equations of motion of wind tunnel virtual flight and free flight to identify and correct the lift, drag derivatives, pitch moment derivatives, and velocity derivatives, respectively. To establish an aerodynamic parameter identification model, the flight dynamics equation is expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. To ensure the identification accuracy of each aerodynamic derivative, an excitation signal design method based on amplitude–frequency characteristic analysis is proposed. The longitudinal aerodynamic parameter identification results of a blended-wing-body aircraft show that identification results with higher accuracy can be obtained by adopting the proposed identification and correction method.

In the early stages of aircraft design, a scaled model of the aircraft is installed in a wind tunnel for dynamic semi-free flight to approximate real flight, and the test data are then used to identify the aerodynamic parameters. However, the absence of the translational motion of the test model makes its flight dynamics different from those in free flight, and the effect of this difference on parameter identification needs to be investigated. To solve this problem, a 3-DOF wind tunnel virtual flight test device is built to fix the test model on a rotating mechanism, and the model is free to rotate in three axes through the deflection of the control surfaces. The flight dynamics equations for the wind tunnel virtual flight test are established and expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. The differences between wind tunnel virtual flight and free flight are analysed to develop a model for the identification of aerodynamic parameters. The selection of the lateral-directional excitation signal and the design method of its parameters are established based on the requirements for the identifiability of the aerodynamic derivatives, and a step-by-step method for the identification of aerodynamic force and moment derivatives is established. The aerodynamic parameter identification results of a blended wing body aircraft show that the identification method proposed in this paper can obtain results with high accuracy, and the response of the modified motion model is consistent with that of the free flight motion model.

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation.