Quadcopter Unmanned Aerial Vehicle Formation Trajectory Tracking based on Extended State Observer with Model Predictive Control

To address the formation trajectory tracking of Quadcopter Unmanned Aerial Vehicle (QUAV) in the complex environment, this paper integrates a Model Predictive Control (MPC) method with an Extended State Observer (ESO), which effectively solves instability of flight coordination resulting from external disturbances and internal uncertainties. The prediction model of the MPC controller is obtained based on Taylor series expansion and Euler transform. ESO estimates external disturbances and internal uncertainties in real-time, adjusts the desired speed via proportional compensation, thereby achieving precise control of formation trajectories. Simulation results demonstrate that the proposed method efficiently suppress external disturbances and internal uncertainties, thereby enabling precise tracking of the trajectory for the formation.

I. Introduction

In recent years, QUAV has gained widespread adoption in both military and civilian applications due to their high flexibility, efficiency, and cost-effectiveness [1]. However, as application scenarios become increasingly complex, a single QUAV is no longer able to fulfill diverse task requirements. Consequently, using a cluster of QUAVs instead of a single one can significantly enhance the efficiency and effectiveness of task completion [2].