Neuro-Adaptive Fault-Tolerant Attitude Control of a Quadrotor UAV With Flight Envelope Limitation and Feedforward Compensation
针对四旋翼无人机在飞行包线限制、外部干扰、模型不确定性和执行器故障下的姿态控制问题，提出一种结合Nussbaum函数和非线性扰动观测器的自适应神经控制方法，实现鲁棒跟踪。
To address the challenges posed by flight envelope limitation, external disturbances, model uncertainties and actuator failures in quadrotor unmanned aerial vehicles (UAVs), we propose an adaptive neural attitude control method that incorporates a Nussbaum function and nonlinear disturbance observer (NDO). By designing the Nussbaum function, we effectively address potential actuator failures while leveraging the NDO enables us to employ feedforward compensation strategy to mitigate perturbation effects. To handle the flight envelope limitation and model uncertainties, we introduce a nonlinear state-dependent function (NSDF) and neural networks (NNs), respectively. The NSDF is utilized to directly constrain the attitude, while the NNs are constructed to estimate the unknown components. Simulation results demonstrate that this approach successfully addresses the flight envelope limitation and maintains robust tracking performance even in the presence of external disturbances, model uncertainties and actuator failures in the controlled system.