This paper 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 paper adopts the online-trained radial basis function neural network (RBFNN) to ensure the expected CC output for battery charging, which aims to handling negative impacts of the continuouslyvaried coupling effect, the disturbance of parameters, and the change of charging current. In this paper, both the simulated and experimental results are given to verify the effectiveness of the proposed control scheme, wherein the accuracy of controlled output current is within 5% and the average response time is less than 100ms. 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 adopted RBFNN-based controller, namely the increased rapidity and the enhanced robustness.     «

This paper 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 paper adopts...     »