The paper presents an effective approach for the design of a flutter suppression system by means of Recurrent Neural Networks (RNNs). The controller is used to move flutter instabilities outside the flight envelope of an unconventional three surface, transport aircraft configuration. The design process requires a comprehensive aircraft model, where flight mechanics, structural dynamics, unsteady aerodynamics and control surface actuators are represented in state-space form, according to the "modern" aeroelastic approach. The control system implemented for flutter suppression is based on two RNNs: one is trained to Identify system dynamics; the other works as a controller using an indirect inversion of the identified model. Keeping the training of both RNNs "on line" leads to an adaptive control system. Extensive numerical tests are used to tune the neural network design parameters and to show how the neural controller increases system damping, widening the flutter-free flight envelope by more than 15% of the uncontrolled flutter velocity.

Active Flutter Suppression for a Three Surface Transport Aircraft by Recurrent Neural Networks

MATTABONI, MATTIA;QUARANTA, GIUSEPPE;MANTEGAZZA, PAOLO
2007-01-01

Abstract

The paper presents an effective approach for the design of a flutter suppression system by means of Recurrent Neural Networks (RNNs). The controller is used to move flutter instabilities outside the flight envelope of an unconventional three surface, transport aircraft configuration. The design process requires a comprehensive aircraft model, where flight mechanics, structural dynamics, unsteady aerodynamics and control surface actuators are represented in state-space form, according to the "modern" aeroelastic approach. The control system implemented for flutter suppression is based on two RNNs: one is trained to Identify system dynamics; the other works as a controller using an indirect inversion of the identified model. Keeping the training of both RNNs "on line" leads to an adaptive control system. Extensive numerical tests are used to tune the neural network design parameters and to show how the neural controller increases system damping, widening the flutter-free flight envelope by more than 15% of the uncontrolled flutter velocity.
48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
1563478927
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/250065
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