Model predictive control (MPC) is increasingly applied in quadrotor unmanned aerial vehicle (UAV) control due to its ability to explicitly handle constraints and its other advantages. However, existing research has yet to consider the impact of aligning the prediction horizon with the real-time velocity of the UAV on trajectory tracking control performance. To address this issue, this paper first analyzes how the prediction horizon influences tracking accuracy under various speed conditions. Based on this analysis, an adaptive prediction horizon model predictive control (APH-MPC) strategy is proposed. This approach determines the optimal prediction horizon parameters for MPC at different target velocities using the grey relational analysis (GRA), and derives the optimal prediction horizon control law via third-order polynomial fitting. Furthermore, an event-triggered mechanism is introduced to enhance the real-time performance of MPC. Experimental results demonstrate that APH-MPC maintains accurate trajectory tracking for UAVs under varying speed conditions while reducing computational cost. This study improves the robustness of MPC in UAV trajectory tracking control.
Bao et al. (Wed,) studied this question.