2 research outputs found
Transmitting Double-D Coil to Wirelessly Recharge the Battery of a Drone with a Receiving Coil Integrated in the Landing Gear
No full textThe aim of this work is the design of a 200 W transmitting coil for a high-power wireless power transfer (WPT) system based on magnetic resonant coupling (MRC) to charge the battery of a drone in 1 h equipped with a WPT receiving coil integrated into the landing gear. This innovative solution is based on the use of the landing gear as the receiving coil, thereby obviating the need for an additional component (e.g., separate receiving coil). The proposed landing gear is fabricated from aluminum, to reduce weight, and to improve mechanical robustness and electrical performance. Consequently, the design reduces overall weight and system complexity while minimizing potential destabilization of the drone’s flight dynamics. However, a specific design of the primary coil is required to ensure high efficiency even in case of an inaccurate landing of the drone on a ground pad. To this aim, a double-D configuration is here proposed and optimized for the transmitting coil, while a double coil receiver in combination with a charge controller that uses a maximum power point tracking (MPPT) algorithm is integrated into the landing gear. The results obtained from the simulations demonstrate that the proposed WPT system has excellent electrical efficiency and very high tolerance to coil misalignment in terms of the coupling coefficient due to imprecise landing. The transmission efficiency of the final test prototype can reach 95% with a coupling coefficient of k = 0.16, and it can drop to a minimum of 85% when misalignment occurs resulting in k = 0.06
Using Machine Learning to Quantify the Robustness of Network Controllability
No full textThis paper presents machine learning based approximations for the minimum number of driver nodes needed for structural controllability of networks under link-based random and targeted attacks. We compare our approximations with existing analytical approximations and show that our machine learning based approximations significantly outperform the existing closed-form analytical approximations in case of both synthetic and real-world networks. Apart from targeted attacks based upon the removal of so-called critical links, we also propose analytical approximations for out-in degree-based attacks.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Network Architectures and Service
