Adaptive control improves drone flight accuracy under changing conditions
L1-MPPI: L1 Adaptive Model Predictive Path Integral for Agile UAV Control
Robotics
Summary
Drones often struggle to follow their planned paths accurately when carrying different weights or facing unexpected wind. This work combines two control methods to help drones adjust in real time to such changes, keeping their flight smooth and on track at high speeds. The researchers showed that their approach works better than previous methods, even when the drone’s weight increased by over a third. This means drones can fly faster and more reliably despite carrying extra loads or facing uncertain conditions.
What this means in practice
- •For drone operators: Maintain precise drone flight paths despite unknown payloads and disturbances for improved delivery or inspection missions.
- •For robotics engineers: Design UAV flight controllers that adapt dynamically to changing mass and aerodynamic effects for robust agile control.
Authors
Lukáš Kotek, Ondřej Procházka, Vojtěch Vonásek, Martin Saska, Robert Pěnička
Abstract
This work proposes the L1 Adaptive Model Predictive Path Integral (L1-MPPI). It cascades L1 adaptive control with the Model Predictive Path Integral (MPPI) to improve tracking of high-speed UAV trajectories. Thanks to the L1augmentation, the tracking remains accurate even under model uncertainties and external disturbances, such as an additional payload or a mismatch in the modeled aerodynamic drag. In contrast to existing MPPI approaches for UAV control that do not explicitly model aerodynamic effects, varying payloads, and typically neglect the dynamics of low-level motor controllers, our L1-MPPI approach enhances the dynamic model used in the MPPI by incorporating the low-level flight controller and motor dynamics, as well as an iterative mixing scheme that reflects the approach of the low-level controller. The proposed method demonstrates improved tracking performance in both simulation and the real world, even when the UAV is subjected to an unknown payload. In flight with 35% mass increase, our approach lowers the RMSE by 58.61% with respect to plain MPPI. Compared to the same MPPI using an online mass estimator in place of the L1 augmentation, the RMSE is lower by 38.59%. During the real-world experiments the UAV reaches speeds up to 13.50 m/s and accelerations up to 2.5 g.