Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Abstract

 As the low-altitude economy rapidly expands, the demand for UAVs is increasingly growing, and their operational scenarios are becoming more complex, with higher requirements for endurance and short-distance take-off and landing performance. Tiltrotor UAVs, characterized by vertical take-off and landing and long endurance, have attracted widespread attention for their potential applications. However, the dynamics and flight paths of tiltrotor UAVs are highly nonlinear, and traditional linear flight controllers cannot fully utilize the real-time performance capabilities of tiltrotor UAVs. Under the conditions of model uncertainty and input saturation in tiltrotor UAVs, traditional LOS+PID control strategies exhibit characteristics of insufficient responsiveness and excessive overshoot. To improve the performance of tiltrotor UAVs in completing path tracking tasks, we have developed a new control strategy. By establishing an error model for three-dimensional space path tracking, we propose a cascaded control strategy of motion controllers and dynamic controllers. The motion controller is designed based on model predictive control, generating a series of speed-limited signals. Then, in the dynamic controller part, an adaptive radial basis function neural network is used to estimate the model uncertainty caused by aerodynamic parameters to enhance its robustness. Finally, the proposed algorithm is compared with the LOS guidance method and PID controller through simulation experiments. The comparison results show that the proposed algorithm can improve the path tracking effect, increase the response speed, and reduce the overshoot.