Feedback Linearization for Quadrotors with a Learned Acceleration Error Model

TL;DR

This paper improves quadrotor control by integrating a learned acceleration error model and a thrust delay mitigation, enhancing robustness and response in real-world conditions through simulation and hardware tests.

Contribution

It introduces a learned acceleration error model into feedback linearization controllers and incorporates a thrust delay model, addressing robustness issues in real systems.

Findings

Enhanced control performance with learned error model

Improved step response on hardware systems

Robustness to unmodeled dynamics and disturbances

Abstract

This paper enhances the feedback linearization controller for multirotors with a learned acceleration error model and a thrust input delay mitigation model. Feedback linearization controllers are theoretically appealing but their performance suffers on real systems, where the true system does not match the known system model. We take a step in reducing these robustness issues by learning an acceleration error model, applying this model in the position controller, and further propagating it forward to the attitude controller. We show how this approach improves performance over the standard feedback linearization controller in the presence of unmodeled dynamics and repeatable external disturbances in both simulation and hardware experiments. We also show that our thrust control input delay model improves the step response on hardware systems.