Adaptive neural fault-tolerant control of a quadrotor UAV via fast terminal sliding mode

The external disturbance, model uncertainty and actuator faults can affect performance even the safety of flight. To tackle such problems, an adaptive fault-tolerant control method is presented integrated with fast terminal sliding mode control (FTSMC) technology and neural network (NN) for the attitude system of a quadrotor unmanned aerial vehicle, where the NN is employed to approximate the uncertain terms in the system. First, for the loss of effectiveness, an adaptive law is developed to estimate the unknown fault coefficient. Then considering the unknown upper bound of the total disturbance, an adaptive law is designed to predict the unknown upper bound. Furthermore, considering the transient response, a FTSMC scheme is developed for the attitude system, which has better convergence rate than conventional terminal sliding mode control. Finally, the contrast test is performed to verify the effectiveness and superiority of the proposed control method. (C) 2022 Elsevier Masson SAS. All rights reserved.

Automated summary

An adaptive fault-tolerant control method integrated with fast terminal sliding mode control technology and neural network is proposed for the attitude system of a quadrotor unmanned aerial vehicle. The neural network is used to approximate the uncertain terms in the system. Adaptive laws are developed to estimate the unknown fault coefficient and predict the unknown upper bound of the total disturbance. A fast terminal sliding mode control scheme is developed for the attitude system to improve the convergence rate. The effectiveness and superiority of the proposed control method are verified through contrast tests.