Adaptive neural network dynamic surface control of hypersonic vehicle with variable geometry inlet

In this paper, a longitudinal model of hypersonic vehicle with variable geometry inlet is established. Because of the strict requirements for the angle of attack of the scramjet during the flight of the aircraft, the uncertainty introduced by the parameter fitting, the rotating lip cover in the long...

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Bibliographic Details
Published in2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) pp. 902 - 907
Main Authors Mengqi, Qiu, Yanze, Hou, Changxiu, Liu, Shaohua, Qiu
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.11.2022
Subjects
Adaptation models
Aerodynamics
Atmospheric modeling
Dynamic surface control
Geometry
Hypersonic vehicle
Lips
Neural networks
Radial basis function (RBF) neural network
Rotatable lip cover
Surface fitting
Variable geometry inlet
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Summary:In this paper, a longitudinal model of hypersonic vehicle with variable geometry inlet is established. Because of the strict requirements for the angle of attack of the scramjet during the flight of the aircraft, the uncertainty introduced by the parameter fitting, the rotating lip cover in the longitudinal model, and the uncertain external interference of the aircraft. The dynamic surface control technology is used to design the angle of attack autopilot of the aircraft and the Radial basis function (RBF) neural network is used to realize the adaptive approximation of the uncertain part of the model, to suppress the interference and accurately track the instructions. Finally, the simulation results show that the method can effectively control the angle of attack of hypersonic vehicle with variable geometry inlet, meet the performance requirements and verify the correctness of the method.
DOI:10.1109/YAC57282.2022.10023696