Research on Quadrotor Trajectory Tracking Based on Model Predictive Control

Quadrotors (UAVs) are widely used in intelligent inspection, environmental monitoring, and logistics due to their simple structure, strong maneuverability, and vertical take-off and landing capabilities. However, their highly nonlinear, strongly coupled, and highly constrained dynamic characteristics make trajectory tracking control a challenging task. To improve trajectory tracking accuracy and control robustness, this paper proposes a quadrotor trajectory tracking method based on model predictive control (MPC). First, a six-degree-of-freedom dynamic model of the quadrotor is established and linearized with small disturbances to transform it into a state-space model suitable for MPC design. An MPC optimization controller is then constructed, with an objective function that minimizes state error and imposes an input energy penalty, while explicitly considering the system's input and state constraints. Simulation results demonstrate that this method exhibits good tracking accuracy and control smoothness for typical trajectory tracking tasks (such as circular and spiral trajectory tracking). Compared with traditional PID and LQR controllers, the proposed method significantly improves maximum error, mean square error, and interference rejection. This study provides an engineering-feasible optimization control framework for UAV trajectory control.