Deep Learning-Based Surrogate Model for Flight Load Analysis

Flight load computations(FLC)are generally expensive and time-consuming.This paper studies deep learning(DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures.We mainly analyze the influence of Mach number,overload,angle of attack,elevator deflection,altitude,and other factors on the loads of key monitoring components,based on which input and output variables are set.The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data.According to the FLC features,a deep neural network(DNN)and a random forest(RF)are proposed to establish the surrogate models.The DNN meets the FLC accuracy requirement using rich data sources in the FLC;the RF can alleviate overfitting and evaluate the importance of flight parameters.Numerical experiments show that both the DNN-and RF-based surrogate models achieve high accuracy.The input variables importance analysis demonstrates that vertical overload and elevator deflection have a significant influence on the FLC.We believe that synthetic applications of these DL-based surrogate methods show a great promise in the field of FLC.

弹性飞行器飞行载荷与动态特性分析(英文)

Based on the equations of motion of flexible air vehicles includingrigid-body modes and elastic structural modes, and applying influence coefficients of linearaerodynamics, a set of equations are derived and a method is presented for analysis of flight loadsand dynamic characteristics. The problems in the fields of flight mechanics and aeroelasticity suchas static aeroelastic divergence, trim and deformation, aerodynamic loads distribution, flutter andflight dynamics can be solved by the procedure. An airplane with high aspect ratio wings isanalyzed, and the results show that the coupling between rigid -body modes and elastic modes isdistinct and should not be overlooked.

Adaptive Fuzzy Torque Control of Passive Torque Servo Systems Based on Small Gain Theorem and Input-to-state Stability

Passive torque servo system （PTSS） simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control （AFTC） algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno （T-S） fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator （ELS）, and the comparative experimental results indicate that AFTC controller is effective for PTSS.