Adaptive Robust Trajectory Tracking Controller for a Quadrotor UAV With Uncertain Environment Parameters Based on Backstepping Sliding Mode Method

In order to improve the trajectory tracking accuracy and environmental adaptability of a quadrotor unmanned aerial vehicle (UAV), an adaptive robust trajectory tracking controller for a quadrotor UAV with uncertain environment parameters based on Backstepping sliding mode method is proposed. This controller uses adaptive estimates to approximate the unknown parameters in the system model and designs an anti-interference link to counteract the negative impact of the external environment, which compensates the control input of the UAV rotor and improves the accuracy and stability of the quadrotor UAV trajectory tracking. Firstly, the mathematical model of the quadrotor UAV in the interference environment is established. Secondly, the tracking target of the quadrotor UAV is divided into attitude target and position target. Then, the adaptive method and Backstepping sliding mode method are used to design the update laws of the estimates and the rotor control input equations. Meanwhile, the appropriate Lyapunov function is established to verify the asymptotic stability of the UAV attitude system and position system. Finally, simulation experiments are carried out to compare the trajectory tracking performance of the quadrotor UAV with various controllers, and the effectiveness and superiority of the proposed controller are verified. Note to Practitioners —This paper mainly focuses on studying a trajectory tracking controller suitable for the dynamics of quadrotor UAVs, especially its application in resisting unknown environmental parameters and disturbances. In practice, adaptive predictive laws are used to estimate model parameter perturbations, and this controller is applied to the rotor control in yaw, pitch, and roll directions, enabling the UAV to track the established reference trajectory. Meanwhile, our backstepping sliding mode controller can effectively improve the robustness of the UAV, ensuring flight accuracy while reducing interference from environmental parameters. In flight control, it is worth studying to solve the external wind field interference and modeling deviation. Parameter prediction based on adaptive methods can effectively handle these unknown variables and prevent UAVs from oversteer in complex environments. The simulation results have verified that the proposed controller can enable the UAV to track the artificially set spiral ascent trajectory, demonstrating high application value in interference environments. In the future, we plan to design an intelligent control method that does not rely on physical modeling, rather than precise models. With this change, we will further improve the application realizability of quadrotor UAV in real world scenarios.