In order to enable wing morphing (e.g. change in camber or folds) without incurring additional weight to the aircraft, lightweight flexible materials such as membrane are needed. However, the research on fluid-structure coupling of membranes has mainly focused on parachutes in low-speed conditions, while that in supersonic flow conditions is lacking. Here, the degraded shell method is proposed to study membrane deformation by using shell element, which is more effective than using membrane elements directly. A fluid-structure interaction computational framework is proposed, whereby the aerodynamic module is composed of either the piston theory or computational fluid dynamics. A rectangular membrane of length 0.4 m and width 0.6 m is investigated in supersonic conditions. The characteristics of the limit cycle and steady deformation are analyzed, considering the effects of angle of attack and dynamic pressure. It is found that the structural response exhibits significant differences under various angles of attack. Furthermore, initial relaxation of membrane has significant influence on the structural deformation. Finally, the aeroelastic scaling method for membrane structures is derived, providing guidance for the design of wind tunnel models. This study provides a theoretical foundation for the analysis and application of membrane structures under supersonic conditions in future research.

The issue of nonlinear structural freeplays in aircraft has always been a significant concern. This study investigates the aeroelastic characteristics of a twin-tail boom Unmanned Aerial Vehicle (UAV) with simultaneous freeplay nonlinearity in its left and right rudders. A comprehensive Limit Cycle Oscillation (LCO) solution route is proposed for complex aircraft with multiple freeplays, which can consider both accuracy and efficiency. For the first time, this study reveals the unique LCO characteristics exhibited by twin-tail boom UAVs with rudder freeplays and provides simulations and explanations of interesting phenomena observed during actual flight. The governing equations are established using the free-interface component mode synthesis method, and the LCOs of the system are mainly solved through the improved time-domain numerical continuation method and frequency-domain numerical continuation method. Furthermore, the study investigates the influence of the left and right rudder freeplay size ratio on the LCO characteristics. The results demonstrate that the twin-tail boom UAV exhibits two stable LCO types: close and differing left and right rudder amplitudes. The proposed method successfully describes the complete LCO behaviors of the system. Overall, this study makes significant contributions to our understanding of the aeroelastic behavior of twin-tail boom UAVs with rudder freeplays.

Structural nonlinearities such as freeplay will affect the stability and even flight safety of the fin-actuator system. There is a lack of a practical method for designing Active Flutter Suppression (AFS) control laws for nonlinear fin-actuator systems. A design method for the AFS controller of the nonlinear all-movable fin-electromechanical actuator system is established by combining the inverse system and the Immersion and Invariance (I&I) theory. First, the composite control law combining the inverse system principle and internal model control is used to offset the nonlinearity and dynamics of the actuator, so that its driving torque can follow the ideal signal. Then, the ideal torque of the actuator is designed employing the I&I theory. The unfavorable oscillation of the fin is suppressed by making the output torque of the actuator track the ideal signal. The simulation results reveal that the proposed AFS method can increase the flutter speed of the nonlinear fin-actuator system with freeplay, and a set of controller parameters is also applicable for wider freeplay within a certain range. The power required for the actuator does not exceed the power that can be provided by the commonly used aviation actuator. This method can also resist a certain level of noise and external disturbance.

This paper proposes a neural network-based intelligent feedforward gust alleviation framework, which includes a neural network identification model and a neural network controller. A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight. A neural network identification model is first trained to accurately predict the aircraft’s output. Then, based on the output of the identification model and the collected flight data, the parameters of the time-delay neural network controller are obtained through a learning process. The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation. For example, when the aircraft is 40000 kg and the flight speed is 0.81Ma, the controller achieves a 67.82% reduction in wingtip acceleration and a 35.90% reduction in center of mass acceleration under continuous turbulence excitation. When considering the measurement uncertainties, such as noise existing in the collected data, the trained controller can still achieve an acceptable gust alleviation effect. Finally, considering a flight in which the aircraft mass is constantly changing, the intelligent controller, which continuously learns from new flight data, maintains a good gust alleviation effect throughout the flight.