Neural network based adaptive nonsingular practical predefined-time fault-tolerant control for hypersonic morphing aircraft

• Published in: Chinese journal of aeronautics Vol. 37; no. 4; pp. 421 - 435
• Main Authors: XU, Shihao, WEI, Changzhu, ZHANG, Litao, MU, Rongjun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024; School of Astronautics,Harbin Institute of Technology,Harbin 150001,China%Beijing Institute of Control & Electronics Technology,Beijing 100038,China
• Subjects: Adaptive control; Fault-tolerant control; Hypersonic morphing aircraft (HMA); Neural network (NN); Practical predefined-time control; Hypersonic morphing aircraft (HMA); Neural network (NN); Practical predefined-time control; Fault-tolerant control; Adaptive control; Hypersonic morphing air-craft(HMA); Neural network(NN)
• ISSN: 1000-9361
• DOI: 10.1016/j.cja.2023.12.020

This paper develops a novel Neural Network (NN)-based adaptive nonsingular practical predefined-time controller for the hypersonic morphing aircraft subject to actuator faults. Firstly, a novel Lyapunov criterion of practical predefined-time stability is established. Following the proposed criterion, a tangent function based nonsingular predefined-time sliding manifold and the control strategy are developed. Secondly, the radial basis function NN with a low-complexity adaptation mechanism is incorporated into the controller to tackle the actuator faults and uncertainties. Thirdly, rigorous theoretical proof reveals that the attitude tracking errors can converge to a small region around the origin within a predefined time, while all signals in the closed-loop system remain bounded. Finally, numerical simulation results are presented to verify the effectiveness and improved performance of the proposed control scheme.