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Position Identification for UAV Wireless Charging Coupler Using Neural Network and Voltage Fingerprint
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Position Identification for UAV Wireless Charging Coupler Using Neural Network and Voltage Fingerprint
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Journal Article
Position Identification for UAV Wireless Charging Coupler Using Neural Network and Voltage Fingerprint
2026
Overview
In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability.
Publisher
MDPI AG
