Dynamic event-triggered robust model predictive control for quadrotor trajectory tracking

This paper addresses the trajectory tracking problem for a full-state quadrotor subject to physical model constraints and unknown external disturbances. A robust tube-based model predictive control (MPC) approach is successfully applied to the system, which is subject to bounded disturbances and hard constraints. In the literature, to reduce the computational complexity of standard time-triggered (ET) MPC without sacrificing performance, ET-MPC has been proposed, solving the optimal control problem only when an event is triggered. In this study, a dynamic threshold set is determined based on the worst-case disturbance effect and the deviation between the actual and predicted states of the quadrotor. Additionally, the discrete-time model of the quadrotor is extended with integral action, enabling the quadrotor to track the reference trajectory without error. To demonstrate the effectiveness of the proposed method, simulation results for time-triggered tube MPC and tube-based dynamic ET-MPC are compared. The proposed method proves its computational efficiency without compromising trajectory tracking performance. Moreover, the dynamic event trigger reduces the computational load 65%–85%, with an acceptable level of control performance degradation.