Adaptive preassigned-time controller design for a hypersonic vehicle with improved performance and uncertainties. (January 2023)

• Record Type: Journal Article
• Authors: Wang, Fang; Wen, Linzhi; Zhou, Chao; Hua, Changchun
• Is Part Of: ISA transactions. Volume 132(2023)
• Page Start: 309
• Page End: 328
• Publication Date: 2023-01
• Subjects: HSV -- Preassigned-time prescribed performance -- Backstepping control -- Fixed time filter; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2022.05.040
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The tracking control problem of a hypersonic vehicle (HSV) under tracking error constraints and uncertainties is investigated. To achieve tracking error converges to its preselected constrained boundary in preassigned time, a performance function is proposed. The preassigned time can be determined by requirement of HSV in prior. Then an adaptive preassigned-time prescribed performance (PTPP) dynamic inversion controller and an adaptive PTPP backstepping controller are proposed for velocity and altitude subsystems, respectively. At the level of control design for altitude subsystem, a dynamic gain fixed-time filter (DGFTF) is proposed for solving 'explosion of complexity' problem. It can ensure that the filter error converges to an arbitrarily small vicinity of zero in fixed time. What is more, it is proved that the proposed PTPP controller can assure predefined performance of velocity and altitude tracking errors in a given preselected time. Finally, compared simulation is performed to validate the effectiveness of the proposed control strategy. Highlights: Adaptive predetermined-time prescribed performance controller renders the stable tracking of velocity and altitude. Predetermined-time performance function makes tracking error converges to the prescribed boundaries in predefined time. Constraint boundaries can be either symmetric or asymmetric, which improves the flexibility of the performance function. Dynamic gain filter solves the 'explosion of complexity' problem, and the filter error converges in fixed time.