Gust Response Alleviation with Model-Free Adaptive Control Method in Transonic Flow

Abstract

In transonic flow, nonlinear problems due to shock waves could be induced by gusts, leading to difficulty in acquiring a relatively accurate aerodynamic model in the form of a state equation for gust alleviation, which is usually required for certain traditional control methods. In addition, using direct computational fluid dynamics (CFD) methods for simulation would be unacceptable for time cost when dealing with the control law design. A data-driven model-free adaptive control (MFAC) method is introduced to alleviate the gust response in transonic flow. The MFAC method transforms the control system into a dynamic linearized data model that exclusively uses input/output data, making the system more efficient. We investigated the influence of different gusts on the aerodynamic result for the rigid airfoil and the aeroelastic result for the elastic airfoil in a subcritical state in the transonic region. An unstable state with prominent fluctuating responses could be induced by certain types of gusts. By applying the MFAC method, the gust responses are remarkably reduced in all the study cases, and some unstable states are almost completely suppressed in certain cases. To achieve economic viability for aircraft operation, passenger aircraft and transport planes are generally designed to cruise in the transonic regime to provide fast travel times while keeping fuel costs reasonable. However, in recent years, injuries due to turbulence on flights of passenger aircraft and transport planes have been reported. When using conventional control methods, the nonlinear phenomena encountered in transonic flows can present challenges for ensuring passenger comfort and reducing the risk of injury when the aircraft encounters turbulence or wind gusts. With many control methods, it is difficult to establish an accurate mathematical model to ensure adequate control performance. Moreover, the stability and robustness of the control system cannot be guaranteed due to uncertainties related to model perturbation and environmental disturbances. To avoid such issues, a data-driven MFAC method that exclusively uses input/output data is introduced in this study, and a case study is presented to verify the performance of the proposed method. The results demonstrate the effectiveness of the method in dealing with transonic gust alleviation problems and verify the feasibility of the proposed method in practical applications.