This article proposes a scheme of space vector optimization for model predictive control (MPC) of dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to restrain the current harmonics under the condition of low inductance. The multiphase electric motor possesses the characteristics of quick current variation rate by means of the low inductance. Especially incorporating with MPC schemes, its advantage of quick response shows promising prospects for various applications such as robotics, aerospace and medical devices. However, such a characteristic of low inductance requires shorter control period, which means that the traditional MPC cannot be implemented to suppress the current harmonics since the performance of the power switching devices and the digital controller limits the arbitrary increasing of the control frequency, which has nearly unexplored in previous studies. In order to address such an issue, this article presents the space-vector-optimized model predictive control (SVO-MPC) for the DTP-PMSMs with low inductance by presynthesizing space vectors to eliminate harmonics in the harmonic frame and optimizing the zero vector to deal with quick current response. As a result, the proposed SVO-MPC can remarkably improve the steady and dynamic control performance, while the traditional MPC almost fails at commonly-adopted control frequency. Finally, simulated and experimental results are both given to verify the feasibility of the proposed SVO-MPC for DTP-PMSMs with quick current response.

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.

This paper presents a compensation network with multiple resonating frequencies for multiple-pickup wireless power transfer (WPT) systems, which aims to establish multiple channels to deliver the power to multiple loads simultaneously. In previous studies, a number of efforts have been made to realize the multichannel transmission by utilizing multiple transmitting coils or transformer to deliver power at different frequencies, which inevitably results in the extra power loss. Although loads can be energized simultaneously, the transmission performance and the maintainability of WPT systems are both deteriorated significantly. In order to address the issue, this paper proposes and implements the multiple-frequency resonating compensation (MFRC) network, which can offer WPT systems with various resonating frequencies. By adopting the power supply with multiple-frequency components, the power can be simultaneously transmitted to loads at corresponding frequencies while avoiding the utilization of transformer or extra transmitting coils. As a result, the proposed MFRC-based multichannel transmission scheme can effectively reduce the power loss and the complexity, as well as increase the transmission efficiency of multiple-pickup WPT systems. In this paper, simulated and experimental results are both given to verify the feasibility of the proposed MFRC network for the multichannel transmission of multiple-pickup WPT systems.

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.

Though the wireless in-flight charging is an ideal way for the energy supply of drones, it also faces the practical challenges, namely dual-disturbance of continuous fluctuation of mutual inductance and battery load variation, changed expected charging power, and the lightweight and no communication design demands for pickup, which have been nearly unexplored in previous article on wireless power transfer (WPT) system. To address the issue, this article proposes a novel communication-free output power control algorithm based on the load identification scheme with automatic frequency adjust. Only the primary current needs to be measured in the proposed algorithm, which can reduce the system complexity with the enhanced real-time performance for WPT system. Simulated and experimental results validate the feasibility of the proposed control algorithm with the identification accuracy of more than 92%, control accuracy of 95%, and average response time within 100 ms. Furthermore, the follow-up of desired output power and the implementation of lightweight pickup demonstrate the flexibility of the power control algorithm, which ensures the rapid and safe energy supply for drones in the drone wireless in-flight charging system.

To address the special challenges for drone wireless in-flight charging systems, namely the continuous fluctuation of mutual inductance, large charging distance, and limited payload of the pickup, a novel high-order primary-series (S) and secondary-single-inductor-double-capacitor (SLDC) topology for parity-time (PT) symmetric wireless power transfer (WPT) system is proposed in this article. The reduced-order model of the proposed system is first established and analyzed based on the coupled-mode theory. It shows the critical coupling coefficient can be reduced by two additional degrees of freedom (λ and L_r ), thus effectively expanding the constant power (CP) region of PT-WPT system. In addition, the inductor L_r can be placed freely in drones and λ can be flexibly adjusted to meet the demands of various environments, which enhance the system flexibility. Also, the communication link and dc–dc converter are not needed, which improve the real time and reduce the drone payload. Simulated and experimental results prove that the transfer distance can be extended by 102% with λ = 0.6 and L_r = 60.5 μH while keeping the CP of 75 W and efficiency of 87.5% against the misalignment and change of the charging distance. Accordingly, with the proposed S/SLDC topology, drone in-flight charging systems can obtain a larger CP region with high efficiency, thus extending the flight time of drones.