Aeroelastic systems have the peculiarity of changing their behavior with flight conditions. Within such a view, it is difficult to design a single control law capable of efficiently working at different flight conditions. Moreover, control laws are often designed on simple linearized, low-fidelity models, introducing the need of a scheduled tuning over a wide operational range. Obviously, such a design process can be time-consuming, because of the high number of simulations and flight tests required to assure high performance and robustness. The present work aims at proving the high flexibility of neural network-based controllers, testing their adaptive properties when applied to typical fixed and rotary-wing aircraft problems. At first, the proposed control strategy will be used to suppress the limit cycle oscillations experienced by a rigid wing in transonic regime. Then, as a second example, a controller with the same structure will be employed to reduce the hub vibrations of an helicopter rotor with active twist blades.
Improvement of Aeroelastic Vehicles Performance Through Recurrent Neural Network Controllers
2016-01-01
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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