This article proposes an adaptive finite-time fault-tolerant control scheme based on a smooth event-triggered mechanism for quadrotor trajectory tracking systems subject to actuator faults and external disturbances. The novel finite-time performance function guarantees the tracking errors are convergent within a prescribed time. The smooth event-triggered mechanism is proposed to overcome the discontinuous triggered signals and decrease transmission resource consumption. Meanwhile, the Zone behavior can be excluded by the positive sampling intervals. Then, the radial basis function (RBF) neural networks are utilized to deal with the model uncertainty, and the adaptive laws are designed to estimate the unknown actuator failures effectively. Finally, an adaptive controller based on the novel time-varying barrier Lyapunov function (BLF) is proposed, and rigorous theoretical analyses are conducted to demonstrate that the tracking errors are convergent within the finite time. Simulation experiments verify the effectiveness of the proposed control scheme.

This article proposes an adaptive fixed-time attitude stabilization control scheme for quadrotor UAVs in the presence of multiple disturbances and uncertainties. Firstly, a novel nonsingular fixed-time terminal sliding mode (NNFTSM) surface is proposed. The dynamic surface guarantees non-singularity and fixed-time convergence so that the setting time is independent of the initial states. Secondly, using the proposed NNFTSM surface and adaptive technique, an adaptive nonsingular fixed-time terminal sliding mode controller (ANFTSMC) is designed for UAVs attitude stabilization. It yields exponential convergence of the attitude tracking errors to zero without requiring a priori knowledge of the upper bounds of the multiple disturbances and uncertainties. Then, the stability of the closed-loop control system is validated by the candidate Lyapunov function, and the upper bound of the convergence time is given. Finally, the parameter design criteria and the convergence time comparison are analyzed in detail. Comparative performances for quadrotor UAV attitude stabilization are presented, and the effectiveness and superiority of the proposed controller are illustrated over the existing method.