LoL-NMPC: Low-Level Dynamics Integration in Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

TL;DR

This paper introduces LoL-NMPC, a novel nonlinear model predictive control method that explicitly incorporates low-level UAV controller dynamics, significantly improving high-speed trajectory tracking accuracy and robustness while maintaining real-time performance.

Contribution

The paper presents a new NMPC formulation that integrates low-level flight controller and motor dynamics, enhancing tracking performance without additional reallocation strategies.

Findings

Achieves 21.97% reduction in tracking error compared to standard NMPC.

Maintains real-time operation at 100 Hz on embedded hardware.

Demonstrates improved accuracy at speeds up to 98.57 km/h and accelerations of 3.5 g.

Abstract

[Accepted to IROS 2025] In this paper, we address the problem of tracking high-speed agile trajectories for Unmanned Aerial Vehicles(UAVs), where model inaccuracies can lead to large tracking errors. Existing Nonlinear Model Predictive Controller(NMPC) methods typically neglect the dynamics of the low-level flight controllers such as underlying PID controller present in many flight stacks, and this results in sub-optimal tracking performance at high speeds and accelerations. To this end, we propose a novel NMPC formulation, LoL-NMPC, which explicitly incorporates low-level controller dynamics and motor dynamics in order to minimize trajectory tracking errors while maintaining computational efficiency. By leveraging linear constraints inside low-level dynamics, our approach inherently accounts for actuator constraints without requiring additional reallocation strategies. The proposed method is validated in both simulation and real-world experiments, demonstrating improved tracking accuracy and robustness at speeds up to 98.57 km/h and accelerations of 3.5 g. Our results show an average 21.97 % reduction in trajectory tracking error over standard NMPC formulation, with LoL-NMPC maintaining real-time feasibility at 100 Hz on an embedded ARM-based flight computer.