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

Abstract

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 suboptimal 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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> 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.