Feedback Linearization for Quadrotors with a Learned Acceleration Error Model

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.

Authors

• Alexander Spitzer
• Nathan Michael

Keywords

• Learning systems
• Deep learning
• Uncertainty
• Logic gates
• Hardware
• Robustness
• Feedback linearization
• Acceleration Error
• Positive Control
• Model System
• Learning Models
• Control Input
• Step Change
• External Disturbances
• Presence Of Control
• Linear Control
• Presence Of Disturbances
• Unmodeled Dynamics
• Error Learning
• Input Delay
• Presence Of External Disturbances
• Hardware Experiments
• Dynamical
• Optimal Control
• Rotational Speed
• Angular Velocity
• Rigid Body
• Angular Acceleration
• Body Frame
• Tracking Error
• Gravity Vector
• Random Frequency
• Extensive Dynamics
• Linear Accelerator
• World Frame
• Adaptive Control
• Position Feedback