AbstractThis paper describes the application of a recurrent dynamic neural network to the problem of adaptive active flutter suppression. A significant feature of the neural controller is its application to an unstable and non‐minimum phase system. The development of the system and its experimental verification are carried out on a wing model with two control surfaces, one on the leading and one at the trailing edge, and two accelerometers. The controller combines a first network for the identification of the variables to be controlled, i.e. the two wing tip accelerations at the leading and trailing edge, with a second network, that commands the deflection required to the control surfaces. It is used for the control task using the output generated by the first network. Adaptivity is obtained by maintaining an on‐line training on both of the neural networks. Extensive preliminary numerical simulations have permitted the authors to define the values of the design parameters aimed at the achievement of an optimal compromise between computational requirements and system performances. The results of the application to the real wing model have shown the effectiveness of such a controller in adapting to different operational conditions, extending flutter free envelope of the wing model up to a speed 30% higher than the uncontrolled flutter on