Fault-tolerant control of UAV anti-skid braking system with input and output constraints

In this paper, a method of adaptive neural network backstepping fault-tolerant control, based on barrier Lyapunov function, is proposed for anti-skid braking system in the presence of slip-ratio constraint, control input saturation and partial loss of actuator effectiveness. The neural network can more accurately approximate the unknown nonlinearity in order to compensate the effect of actuator fault, and the great robustness to actuator fault is guaranteed. In this approach, the output of neural network is used to design the backstepping controller to achieve fault-tolerant control and uncertainty compensation, and a robust term is employed to optimize the transient performance of braking system. Firstly, the closed-loop fault-tolerant control system could be stable without the reconfiguration value of actuator fault in real time. Then, the stability of the system is analyzed based on the Lyapunov method. Finally, the numerical simulation results show that the proposed fault-tolerant control scheme can effectively guarantee the stability and effectiveness of the control system when the actuator happens faulty.