Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a UAV with internal uncertainties, input–output constraints disturbances. Radial basis networks are used to approximate uncertainties dynamic model UAV, while minimum parameter learning method combined accelerate adjustment speed weights. A robust term balance total system disturbance improve anti-interference performance. BLF handle output constraint so that constrained parameters cannot break predefined constraints. An auxiliary introduced solve input saturation avoid dependence tracking error amplitude approximating using smoothing function. stability demonstrated method. simulation results show proposed has high accuracy compared surface method, range.