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

We address the problem of high-speed agile trajectory tracking for Unmanned Aerial Vehicles, where model inaccuracies can lead to large tracking errors. Existing Nonlinear Model Predictive Controller (NMPC) methods typically neglect the dynamics incurred by low-level flight controllers such as angular velocity Proportional Integral Derivative (PID) present in many flight stacks, resulting in suboptimal tracking performance at high speeds and accelerations. To this end, we propose a novel NMPC formulation, LoL-NMPC, which explicitly incorporates the dynamics of the low-level PID flight controller and motor actuation mixing in order to minimize trajectory tracking errors while maintaining computational efficiency. By leveraging the motor mixing matrix, 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  obustness at speeds up to 98.57 km/h and accelerations of 3.5g. 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.