Fractional-Order Sliding-Mode Fault-Tolerant Neural Adaptive Control of Fixed-Wing UAV With Prescribed Tracking Performance

In this paper, a fractional-order sliding-mode fault-tolerant tracking control scheme is proposed for a fixed-wing UAV with prescribed performance. The outer-loop position dynamics is first transformed to the second-order nonlinear model. By using neural networks, the unknown nonlinear functions containing actuator faults are identified. Moreover, the minimum learning parameter of neural networks is constructed to reduce the computational burden. Fractional-order calculus is further utilized in the sliding-mode control for improving the fault-tolerant tracking performance. Simulation results are presented to show the effectiveness.