Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control

TL;DR

This paper introduces a data-driven control method combining deep Koopman operators with model predictive control to enhance quadrotor trajectory tracking and stabilization, achieving high accuracy and low computation time.

Contribution

It develops a novel DK-MPC framework that linearizes quadrotor dynamics in a learned latent space for efficient control, outperforming traditional nonlinear MPC methods.

Findings

Superior tracking accuracy demonstrated in simulations

Reduced computation time compared to nonlinear MPC

Effective handling of complex quadrotor dynamics

Abstract

This paper presents a data-driven control framework for quadrotor systems that integrates a deep Koopman operator with model predictive control (DK-MPC). The deep Koopman operator is trained on sampled flight data to construct a high-dimensional latent representation in which the nonlinear quadrotor dynamics are approximated by linear models. This linearization enables the application of MPC to efficiently optimize control actions over a finite prediction horizon, ensuring accurate trajectory tracking and stabilization. The proposed DK-MPC approach is validated through a series of trajectory-following and point-stabilization numerical experiments, where it demonstrates superior tracking accuracy and significantly lower computation time compared to conventional nonlinear MPC. These results highlight the potential of Koopman-based learning methods to handle complex quadrotor dynamics while meeting the real-time requirements of embedded flight control. Future work will focus on extending the framework to more agile flight scenarios and improving robustness against external disturbances.