LoL-NMPC: Low-Level Dynamics Integration in Nonlinear Model Predictive Control for Unmanned Aerial Vehicles
🤖 AI Summary
In high-speed agile flight, neglecting the dynamics of low-level PID controllers and electric motors introduces significant trajectory tracking errors in nonlinear model predictive control (NMPC). Method: This work explicitly integrates both the low-level flight controller and motor dynamics into the NMPC formulation, leveraging its inherent linear constraints to naturally enforce actuator magnitude and rate limits—thereby eliminating error accumulation and auxiliary allocation strategies typical of hierarchical control architectures. Contribution/Results: The proposed method achieves real-time NMPC optimization at 100 Hz on an ARM-based embedded platform. Experimental validation demonstrates operation at speeds up to 98.57 km/h and accelerations up to 3.5 g, with a 21.97% reduction in mean tracking error compared to conventional approaches. This yields substantial improvements in both robustness and real-time performance while maintaining computational feasibility for onboard deployment.
Application Category
📝 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 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.
Problem
Research questions and friction points this paper is trying to address.
Integrating low-level UAV dynamics into NMPC for accurate trajectory tracking
Addressing model inaccuracies causing large tracking errors in high-speed flights
Maintaining computational efficiency while improving UAV tracking performance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates low-level controller and motor dynamics
Uses linear constraints for actuator limitations
Achieves real-time feasibility at 100 Hz
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