  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.
