Q-learning–based practical disturbance compensation control for hypersonic flight vehicle. (June 2023)

• Record Type: Journal Article
• Authors: Li, Xu; Zhang, Ziyi; Ji, Yuehui; Liu, Junjie; Gao, Qiang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 8(2023)
• Page Start: 1916
• Page End: 1929
• Publication Date: 2023-06
• Subjects: Hypersonic flight vehicle -- attitude control -- super-twisting algorithm -- extended state observer -- Q-learning; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/09544100221140242
• Languages: English
• ISSNs: 0954-4100

Abstract

Aiming at the attitude control problem of hypersonic flight vehicle, a compound control strategy based on the disturbance compensation technique and Q-learning optimization is proposed. Firstly, a three-channel independent control scheme based on the super-twisting extended state observer (STESO) is proposed such that coupling among channels, uncertainties, and external disturbances can be estimated and compensated in real time. Secondly, the Q-learning mechanism is introduced to optimize the control parameters while keeping the control structure unchanged. Lastly, the convergence of the STESO is analyzed by the Lyapunov theory, and some numerical simulation results are carried out to verify the effectiveness of the proposed strategy. Compared with the traditional linear active disturbance rejection control, the proposed method can avoid a lot of manual parameter tuning process and also has better performance.