Cable Optimization and Drag Estimation for Tether-Powered Multirotor UAVs
Max Beffert, Andreas Zell
TL;DR
This work tackles the limited flight time of multirotor UAVs by using tethered power, focusing on optimal cable selection under hover and forward-flight conditions. It introduces a physics-informed, system-identification framework that jointly optimizes tether cross-section and models forward-flight drag, accounting for tether resistance, cable mass, motor efficiency, and drag dynamics. The approach yields a concrete cable-sizing criterion and a drag model that combines quadratic and linear terms with a wind offset, supported by experiments showing the optimal aluminum cable of $A_t=1.09\ \mathrm{mm^2}$ and a tether mass of $0.55\ \mathrm{kg}$ for a $2.26\ \mathrm{kg}$ system, with hover and forward-flight power figures of $P_{hover}=405$ W and $P_{30km/h}=445$ W. The practical impact is improved efficiency, safety, and the potential for real-time control of tethered drones in varied conditions, though future work should refine tether aerodynamics and enable model-predictive control that accounts for tether-ground-vehicle geometry.
Abstract
The flight time of multirotor unmanned aerial vehicles (UAVs) is typically constrained by their high power consumption. Tethered power systems present a viable solution to extend flight times while maintaining the advantages of multirotor UAVs, such as hover capability and agility. This paper addresses the critical aspect of cable selection for tether-powered multirotor UAVs, considering both hover and forward flight. Existing research often overlooks the trade-offs between cable mass, power losses, and system constraints. We propose a novel methodology to optimize cable selection, accounting for thrust requirements and power efficiency across various flight conditions. The approach combines physics-informed modeling with system identification to combine hover and forward flight dynamics, incorporating factors such as motor efficiency, tether resistance, and aerodynamic drag. This work provides an intuitive and practical framework for optimizing tethered UAV designs, ensuring efficient power transmission and flight performance. Thus allowing for better, safer, and more efficient tethered drones.
