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.
@article{arxiv.2105.13527,
title = {Feedback Linearization for Quadrotors with a Learned Acceleration Error Model},
author = {Alexander Spitzer and Nathan Michael},
journal= {arXiv preprint arXiv:2105.13527},
year = {2021}
}