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Abstract

The design of a robust maneuver load alleviation (MLA) system for a high-performance aircraft is studied in this paper. First, the aeroservoelastic (ASE) models of a high-performance military aircraft in climbing maneuver at varying Mach numbers are established. Then, a linear parameter-varying (LPV) model of the ASE systems is constructed and an $H_{\infty}$ robust controller is designed based on the LPV model. The robust control is realized via a pair of outboard ailerons to alleviate the wing-root bending moments in the climbing maneuvers. To compensate the loss of performance in the load alleviation, a controller based on recurrent neural networks is designed in the flight control. Finally, some numerical simulations are made to testify the performance and robustness of the MLA system.

Keywords

Aeroelasticity maneuver load alleviation high performance aircraft robust control recurrent neural network

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Maneuver load alleviation for high performance aircraft robust to flight condition variations

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