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In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.
Yang et al. (Sun,) studied this question.