Fixed-Time Neural Control for Hypersonic Flight Vehicles with Asymmetric Time-Varying Constraints. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Dong, Zehong; Li, Yinghui; Xu, Haojun; Li, Zhe; Pei, Binbin; Zheng, Wuji
• Is Part Of: Journal of physics. Volume 1966:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1966/1/012050
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper presents a novel fixed-time adaptive tracking control scheme for the hypersonic flight vehicles (HFVs) subject to asymmetric time-varying constraints, uncertain dynamics and unknown external disturbances. By incorporating the back-stepping technique and radial basis function neural networks (RBFNNs), the uncertain dynamics of HFVs are estimated. Note that most existing results only achieve practical fixed-time stability but not fixed-time stability, or require specific knowledge of all the dynamics of HFVs. To remove such restrictions, a fixed-time controller is newly constructed by means of a tuning functions and a projection operator-based adaptation mechanism. In consequence, the tracking errors can asymptotically converge to the preassigned compact set within fixed-time and the asymmetric time-varying constraints of HFVs never are violated. Finally, the effectiveness and superiority of the proposed control strategy is demonstrated by numerical simulations.