${\mathcal{L}}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

Abstract

This paper introduces an ${\mathcal{L}}_1$ adaptive control augmentation for geometric tracking control of quadrotors. In the proposed design, the ${\mathcal{L}}_1$ augmentation handles nonlinear (time- and state-dependent) uncertainties in the quadrotor dynamics without assuming or enforcing parametric structures, while the baseline geometric controller achieves stabilization of the known nonlinear model of the system dynamics. The ${\mathcal{L}}_1$ augmentation applies to both the rotational and the translational dynamics. Experimental results demonstrate that the augmented geometric controller shows consistent and (on average five times) smaller trajectory tracking errors compared with the geometric controller alone when tested for different trajectories and under various types of uncertainties/disturbances.

accepted by ICRA 2022

References in corpus (2)

• Geometric Adaptive Tracking Control of a Quadrotor UAV on SE(3) for Agile Maneuvers
• Asynchronous Deep Model Reference Adaptive Control

Cited by in corpus (4)

• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
• Neural Moving Horizon Estimation for Robust Flight Control
• From Propeller Damage Estimation and Adaptation to Fault Tolerant Control: Enhancing Quadrotor Resilience
• Geometric Tracking Control of Omnidirectional Multirotors for Aggressive Maneuvers