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Improvement of aeroelastic vehicles performance through recurrent neural network controllers

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Abstract

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.

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  1. Via La Masa, 34, 20156, Milan, Italy

    Claudio Brillante & Andrea Mannarino

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  1. Claudio Brillante
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  2. Andrea Mannarino
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Correspondence to Andrea Mannarino.

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Brillante, C., Mannarino, A. Improvement of aeroelastic vehicles performance through recurrent neural network controllers. Nonlinear Dyn 84, 1479–1495 (2016). https://doi.org/10.1007/s11071-015-2583-2

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  • DOI: https://doi.org/10.1007/s11071-015-2583-2

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