NN-Based Fixed-Time Attitude Tracking Control for Multiple Unmanned Aerial Vehicles with Nonlinear Faults

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 2; pp. 1 - 11
• Main Authors: Zheng, Xiaohong, Li, Hongyi, Ahn, Choon Ki, Yao, Deyin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Attitude control; Attitude stability; Control systems design; convex optimization; Convexity; Coordinated attitude control; Curve fitting; Fault tolerance; Fault tolerant systems; fault-tolerant control; Faults; fixed-time control; Low pass filters; Lyapunov methods; multiple unmanned aerial vehicles (MUAVs); Neural networks; Nonlinearity; Optimization; Protocols; Stability criteria; Tracking control; Unmanned aerial vehicles
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3205566

This paper focuses on the fixed-time fault-tolerant attitude tracking control problem for multiple unmanned aerial vehicles (MUAVs) with nonaffine nonlinear faults. First, the command filter and neural networks (NNs) are employed to characterize unknown nonlinearities in MUAVs, and the update law of NN is developed via convex optimization technique. Second, the algebraic loop problem caused by nonaffine nonlinear faults is adequately solved by introducing the Butterworth low-pass filter. Then, the curve-fitting method is utilized to construct a piecewise virtual control signal to avoid the singularity problem in fixed-time control. Furthermore, based on the Lyapunov stability theory, a general fixed-time stability criterion is adopted to prove that the designed fault-tolerant attitude controller can guarantee the stability of MUAVs in fixed time. Finally, the effectiveness of the proposed control design method is verified via illustrative examples.