NN-Based Fixed-Time Attitude Tracking Control for Multiple Unmanned Aerial Vehicles with Nonlinear Faults
Xiaohong Zheng, Hongyi Li, Choon Ki Ahn, Deyin Yao
Abstract
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.