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.

Matching design of hydraulic load simulator with aerocraft actuator

This paper intends to provide theoretical basis for matching design of hydraulic load simulator （HLS） with aerocraft actuator in hardware-in-loop test, which is expected to help actuator designers overcome the obstacles in putting forward appropriate requirements of HLS. Traditional research overemphasizes the optimization of parameters and methods for HLS controllers. It lacks deliberation because experimental results and project experiences indicate different ultimate performance of a specific HLS. When the actuator paired with this HLS is replaced, the dynamic response and tracing precision of this HLS also change, and sometimes the whole system goes so far as to lose control. Based on the influence analysis of the preceding phenomena, a theory about matching design of aerocraft actuator with HLS is presented, together with two paired new concepts of ＂Standard Actuator＂ and ＂Standard HLS＂. Further research leads to seven important conclusions of matching design, which suggest that appropriate stiffness and output torque of HLS should be carefully designed and chosen for an actuator. Simulation results strongly support that the proposed principle of matching design can be anticipated to be one of the design criteria for HLS, and successfully used to explain experimental phenomena and project experiences.

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.