DQ-NMPC: Dual-Quaternion NMPC for Quadrotor Flight
Luis F. Recalde, Dhruv Madhusudan Agrawal, Jon Arrizabalaga, Guanrui Li
AI summary
Problem
Conventional NMPC methods for quadrotors often decouple translational and rotational dynamics or rely on local approximations, leading to poor numerical conditioning, slower convergence, and degraded performance during agile maneuvers.
Approach
The authors formulate an NMPC framework that unifies translation and rotation by representing system states and pose errors directly on the dual-quaternion manifold, optimizing a unified cost function in its Lie algebra.
Key results
- Reduces position and orientation tracking errors by up to 56.11% and 56.77% compared to baseline NMPC
- Enables tracking of aggressive trajectories at 13.66 m/s and 4.2 g in confined spaces where baseline fails
- Improves numerical conditioning and reduces optimization iterations in pose regulation tasks
- Demonstrates robustness to model mismatches and external disturbances in simulation
Why it matters
Provides a more reliable and computationally efficient control framework for agile MAV operations in complex, dynamic environments like logistics and emergency response.
Abstract
MAVs have great potential to assist humans in complex tasks, with applications ranging from logistics to emer- gency response. Their agility makes them ideal for operations in complex and dynamic environments. However, achieving precise control in agile flights remains a significant challenge, particularly due to the underactuated nature of quadrotors and the strong coupling between their translational and rotational dynamics. In this work, we propose a novel NMPC framework based on dual- quaternions (DQ-NMPC) for quadrotor flight. By representing both quadrotor dynamics and the pose error directly on the dual-quaternion manifold, our approach enables a compact and globally non-singular formulation that captures the quadrotor coupled dynamics. We validate our approach through simulations and real-world experiments, demonstrating better numerical conditioning and significantly improved tracking performance, with reductions in position and orientation errors of up to 56.11% and 56.77%, compared to a conventional baseline NMPC method. Furthermore, our controller successfully handles aggres- sive trajectories, reaching maximum speeds up to 13.66 m/s and accelerations reaching 4.2 g within confined space conditions of dimensions 11m × 4.5m × 3.65m under which the baseline controller fails. SUPPLEMENTARY MATERIAL Page: https://acp-lab.github.io/dq-nmpc.github.io/