Trajectory Tracking Control of Uncertain Quadrotor UAV
Qiming Li, Ran Shi, Jing‐Jing Xiong · 2024
For the problem of external wind disturbances and model parameter uncertainties that quadrotor unmanned aerial vehicle (UAV) often face during flight in natural environments, an adaptive sliding mode control scheme via recurrent neural network is proposed. Firstly, the quadrotor dynamics system is considered by designing two subsystems, i.e., fully actuated one and underactuated one. Then, the recurrent neural network is used to effectively approximate or adaptively estimate their lumped terms from external wind disturbance and model uncertainty faced by the quadrotor UAV, and continuous feedback compensation sliding mode controllers are designed. Finally, simulation results are given to show the effectiveness of proposed method.