NDO-Based Dual Quaternion Control of a Drone with a Cable-suspended Load
Yuxia Yuan, Junjie Kang, Jinjun Shan, Markus Ryll
AI summary
Problem
Cable-suspended cargo drones suffer from complex nonlinear coupling between the drone and load, making them highly vulnerable to parametric uncertainties and external disturbances that destabilize flight and degrade tracking accuracy.
Approach
The authors derive a hybrid dual quaternion dynamic model that unifies translational and rotational motion while handling both taut and slack cable states, then integrate decoupled nonlinear disturbance observers to estimate and compensate for real-time uncertainties.
Key results
- Hybrid dual quaternion model unifies drone-load dynamics across taut and slack cable conditions
- Three decoupled NDOs accurately estimate and compensate for load, cable, and drone disturbances
- Robust trajectory tracking and swing suppression achieved under parametric uncertainties and wind gusts
- Framework validated through simulations and real-world cargo drone experiments
Why it matters
Provides a reliable, computationally efficient control solution for deploying cargo drones in complex logistics and disaster relief operations where ground access is limited.
Abstract
This paper proposes a novel nonlinear disturbance observer (NDO) based dual quaternion dynamics modeling and control framework for a drone with a cable-suspended load. Leveraging dual quaternions, a compact and singularity- free mathematical representation, we derive a unified dynamic model that captures the coupled translational and rotational dynamics of both the drone and the slung load. NDOs are designed to estimate and compensate for uncertainties and external disturbances affecting the drone and the load. Building on this framework, we develop a robust control strategy that ensures precise trajectory tracking of the slung load while maintaining stable drone attitude control. The effectiveness of the proposed approach is validated through comprehensive simulations and real-world experiments on a cargo drone platform. The results highlight the robustness and reliability of the system in practical scenarios, demonstrating its potential application in cargo transportation.