Adaptive control of transonic buffet flows over an airfoil

Flow state can be changed by multiple disturbances and uncertain factors in a complex flow environment, which calls for great interest to adjust the control law automatically to adapt to the changing flow environment. Model-based control can obtain predetermined control effects, but its adaptive ability is limited due to the modeling accuracy and unmodeled dynamics of the reduced-order model. To overcome these limitations, the data-driven adaptive control of transonic buffet flow based on the radial basis function neural network (RBF-NN) is carried out in this work. The actuator is the trailing edge flap, and the feedback signal is the lift coefficient. The historical input and output are used in the RBF-NN adaptive control to calculate the current control input from the neural network. When the flow state changes, the parameters of the neural network are adjusted by an adaptive mechanism to make the system work in an optimal or a near-optimal state automatically. Results show that buffet loads can be suppressed completely by RBF-NN control, even if the freestream Mach number and the angle of attack change continuously [from (M, α) = (0.7, 5.5°) to (M, α) = (0.8, 1.5°)]. The control strategy proposed in this work only needs the historical response data of the flow field, and it shows little dependence on the low-order linear model of the system. Therefore, it can be applied to the unstable flow control, in which the low-order model of the flow is difficult to construct and automatically adapt to the changing flow environment.