The Adaptive Feed-Forward Control Of Gust Load For An Elastic Aircraft

In this paper, we mainly focus on the gust loads alleviation of a civil airplane. Additional aerodynamics will be excited as an aircraft going through the gust region, which will cause the rigid body motion and elastic vibrations of the elastic aircraft. These vibrations will cause dynamics structural loads, decrease handling qualities and passenger comfort, increase the wing root loads and decrease the fatigue life of the structure. Especially in recent decades, along with the flexibility of aircraft getting bigger and bigger, the frequencies of rigid body motions and aeroelastic vibrations have the tendency to get closer to each other, which increases the nonlinear aeroelastic coupling between flight mechanics and structural dynamics. Therefore, the problems of gust loads alleviation are getting more and more attention of researchers and designers.The gust loads can be efficiently decreased by introducing the gust loads alleviation system. To avoid delays and fluctuations excited by traditional close-loop feedback control strategy, we can use feed-forward control strategy, so that the corrective action can be taken before the output has deviated from the set point.In this paper, by coupling the unsteady aerodynamics and aircraft structure and using the equations of motion and the rational function approximation method, the open-loop aeroservoelastic model, which is under the joint excitation of discrete gust and control surfaces deflection, is established. Then the discrete gust response and the continuous gust response are calculated and compared with each other by using time domain method and hybrid method. Two symmetrical ailerons are used as control surfaces and the unified feed-forward and feedback control law was designed to alleviate the wing-root loads and acceleration response of the aircraft. The coefficients of the FIR controller are changed at each time step. And through the stability analysis, we can ensure that the FIR filter is stable. The simulation results show that the developed adaptive feed-forward control law is efficient and effective.