Adaptive fault-tolerant tracking control of flying-wing unmanned aerial vehicle with system input saturation and state constraints

In this paper, an adaptive fault-tolerant attitude tracking controller based on reinforcement learning is developed for flying-wing unmanned aerial vehicle subjected to actuator faults and saturation. At first, the attitude dynamic model is separated into two dynamic subsystems as slow and fast dynamic subsystems based on the principle of time scale separation. Secondly, backstepping technique is adopted to design the controller. For the purpose of attitude angle constraints, the control technique based on Barrier Lyapunov is used to design controller of slow dynamic subsystem. Considering the optimization of the fast dynamic subsystem, this paper introduces an adaptive reinforcement learning control method in which neural network is used to approximate the long-term performance index and lumped fault dynamic. It is shown that this control algorithm can satisfy the requirements of attitude tracking subjected to the control constraints and the stability of the system is proved from Lyapunov stability theory. The simulation results demonstrate that the developed fault-tolerant scheme is useful and has more smooth control effect compared with fault-tolerant controller based on sliding mode theory.

Automated summary

This paper presents an adaptive fault-tolerant attitude tracking controller based on reinforcement learning for a flying-wing unmanned aerial vehicle. The controller separates the attitude dynamic model into slow and fast dynamic subsystems, utilizes backstepping and Barrier Lyapunov techniques, and incorporates neural networks for optimization. The effectiveness of the control algorithm is proven through Lyapunov stability theory, demonstrating improved performance compared to traditional fault-tolerant controllers.