Mutual-Inductance-Dynamic-Predicted Constant Current Control of <i>LCC-P</i> Compensation Network for Drone Wireless In-Flight Charging
Yu Gu, Jiang Wang, Zhenyan Liang, Zhen Zhang
Abstract
In this article, we propose a mutual-inductance-dynamic-predicted constant current (CC) control to realize the secondary-feedback-free output current adjustment for drone wireless in-flight charging systems. In practical systems, the challenge is to keep a CC output for drones under the continuous fluctuations of mutual inductance, the variation of desired charging current, and the carrying weight limits of drones, which has been nearly unexplored in previous studies on wireless power transfer systems. Accordingly, this article proposes a novel mutual-inductance prediction scheme combined with optimized phase shift control to maintain the desired CC charging output, which can be implemented at the transmitting side to address the impact of the above-mentioned challenges. In the article, simulated and experimental results are both given to verify the feasibility of the proposed control scheme, wherein the prediction accuracy is above 92.5%, the CC control accuracy is within 5%, and the average response time is less than 320 ms. It shows that the proposed dynamic-predicted CC control scheme has improved real-time capability and enhanced robustness, which is an ideal technical means for drone wireless in-flight charging.