TY - JOUR
T1 - Dynamic-Balancing Robust Current Control for Wireless Drone-in-Flight Charging
AU - Zhang, Zhen
AU - Shen, Shen
AU - Liang, Zhenyan
AU - Eder, Stephan H.K.
AU - Kennel, Ralph
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - This article proposes an enhanced timely and robust constant current (CC) control for wireless in-flight charging systems. The challenge for practical wireless in-flight charging systems is to maintain a CC output for hovering drones under circumstances of the continuous variation of coupling effect, various charging power requirements, and the parameter shifting, which is nearly unexplored in previous studies on wireless power transfer technologies. In order to address the issue, this article adopts the online-trained radial basis function neural network (RBFNN) to ensure the expected CC output for battery charging, which aims to handle negative impacts of the continuously varied coupling effect, the disturbance of parameters, and the change of charging current. In this article, both simulated and experimental results are given to verify the effectiveness of the proposed control scheme, wherein the accuracy of the controlled output current is within 5% and the average response time is less than 100 ms. It shows that the proposed dynamic-balancing robust current control is an ideal technical solution for wireless in-flight charging of drones by means of remarkable characteristics of the adopted RBFNN-based controller, namely, the increased rapidity and the enhanced robustness.
AB - This article proposes an enhanced timely and robust constant current (CC) control for wireless in-flight charging systems. The challenge for practical wireless in-flight charging systems is to maintain a CC output for hovering drones under circumstances of the continuous variation of coupling effect, various charging power requirements, and the parameter shifting, which is nearly unexplored in previous studies on wireless power transfer technologies. In order to address the issue, this article adopts the online-trained radial basis function neural network (RBFNN) to ensure the expected CC output for battery charging, which aims to handle negative impacts of the continuously varied coupling effect, the disturbance of parameters, and the change of charging current. In this article, both simulated and experimental results are given to verify the effectiveness of the proposed control scheme, wherein the accuracy of the controlled output current is within 5% and the average response time is less than 100 ms. It shows that the proposed dynamic-balancing robust current control is an ideal technical solution for wireless in-flight charging of drones by means of remarkable characteristics of the adopted RBFNN-based controller, namely, the increased rapidity and the enhanced robustness.
KW - Current control
KW - drones
KW - in-flight charging
KW - wireless power transfer (WPT)
UR - http://www.scopus.com/inward/record.url?scp=85114715450&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2021.3111755
DO - 10.1109/TPEL.2021.3111755
M3 - Article
AN - SCOPUS:85114715450
SN - 0885-8993
VL - 37
SP - 3626
EP - 3635
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 3
ER -