Output constrained adaptive neural control for generic hypersonic vehicles suffering from non-affine aerodynamic characteristics and stochastic disturbances

Autor: Caisheng Wei, Xiaofeng Zhang, Kang Chen, Supeng Zhu, Tao Xu
Rok vydání: 2021
Předmět:
Zdroj: Aerospace Science and Technology. 111:106469
ISSN: 1270-9638
DOI: 10.1016/j.ast.2020.106469
Popis: This paper proposes a novel output constrained non-affine control structure by combining backstepping adaptive control with coordinate transformation technique for Generic Hypersonic Vehicles (GHVs) attitude tracking problem suffering from stochastic uncertainties. Firstly, by introducing several specific nonlinear functions, the output constrained control problem is transformed into a stabilization problem of several new variables. Then a novel adaptive control scheme that employee the boundary estimation technique and Nussbaum-type functions to counteract the effect of non-affine aerodynamic characteristics is integrated to address the attitude tracking problem. Meanwhile, two Neural Networks (NNs) are introduced to approximate and compensate the unknown nonlinearities. With the aid of a four-order Lyapunov function, the closed-loop attitude control system is proved to be stochastically stable. As a result, the output tracking errors can be guaranteed to stay in the predefined constraints during the whole control process and finally converge to an acceptable small value. Comparative simulation results are presented to demonstrate the effectiveness and advantages of the proposed control strategy.
Databáze: OpenAIRE
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