L1-MPPI: L1 Adaptive Model Predictive Path Integral for Agile UAV Control

2026-09-29

AI Summary

This study addresses the insufficient trajectory tracking accuracy of high-speed unmanned aerial vehicles (UAVs) under model uncertainties and external disturbances by proposing a cascaded framework integrating L1 adaptive control with Model Predictive Path Integral (MPPI) control. Methodologically, an iterative blending scheme is employed to explicitly model underlying motor dynamics, effectively compensating for payload variations and aerodynamic drag errors while overcoming the limitation of conventional MPPI in neglecting actuator dynamics. Experimental results demonstrate that under a 35% abrupt mass increase, the trajectory tracking root mean square error (RMSE) is reduced by 58.61%. Furthermore, flight tests achieve velocities of 13.5 m/s and accelerations of 2.5g, significantly enhancing the system's robust high-speed tracking capability under extreme conditions.

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.

Problem

• UAV control
• trajectory tracking
• model uncertainty
• external disturbances
• agile flight

Innovation

• L1 adaptive control
• Model Predictive Path Integral (MPPI)
• UAV agile control
• motor dynamics
• model uncertainty