Feedback Linearization for Quadrotors with a Learned Acceleration Error Model
This work addresses robustness problems for quadrotor control systems, but it is incremental as it builds on existing feedback linearization methods.
The paper tackled the robustness issues of feedback linearization controllers for quadrotors by learning an acceleration error model and mitigating thrust input delays, resulting in improved performance in simulation and hardware experiments, such as enhanced step response.
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.