Quadrotor Trajectory Tracking Using Linear and Nonlinear Model Predictive Control

TL;DR

This paper compares linear and nonlinear model predictive control methods for quadrotor trajectory tracking, demonstrating their effectiveness through detailed modeling and simulation in complex environments.

Contribution

It introduces and evaluates two advanced model-based control frameworks, LMPC and NMPC, for improved quadrotor trajectory tracking.

Findings

Both controllers effectively track trajectories in simulation.

NMPC shows better adaptability to disturbances.

LMPC offers computational efficiency.

Abstract

Accurate trajectory tracking is an essential characteristic for the safe navigation of a quadrotor in cluttered or disturbed environments. In this paper, we present in detail two state-of-the-art model-based control frameworks for trajectory tracking: the Linear Model Predictive Controller (LMPC) and the Nonlinear Model Predictive Controller (NMPC). Additionally, the kinematic and dynamic models of the quadrotor are comprehensively described. Finally, a simulation system is implemented to verify feasibility, demonstrating the effectiveness of both controllers.