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The single-phase high frequency link (HFL) matrix-type DC-AC converter has been widely explored due to its high efficiency, light weight, and high power density, which offers galvanic isolation power conversion without bulky dc link capacitors and soft-switching operation. Different from traditional two-level DC-AC converters, the single-phase HFL matrix-type DC-AC converter has no double-line frequency ripple voltage on the DC side, but the ripple appears on the clamp capacitor, which has not yet been investigated in detail so far. In this paper, the mechanism of the double-line frequency ripple voltage of the clamp capacitor in the single-phase HFL matrix-type DC-AC converter is comprehensively explored, and a mathematical model of the HFL matrix-type DC-AC converter is established. To suppress the effect of the double-line frequency ripple voltage on the power quality and guarantee the good power quality of the AC side, a multi-resonant (MPR) controller is applied to reduce voltage harmonic distortions. A 20-kHz HFL matrix-type DC-AC converter prototype is built. Experimental results have verified the effectiveness of the analysis and applied control scheme.

The wireless power transmission (WPT) system for electric vehicle charging has the advantages of being safe and reliable, without plugging and unplugging, but the random misalignment between the energy transmitting coil and the energy receiving coil will cause uncertain disturbance to the transmission power and system performance. Therefore, it is necessary to improve the robustness of the wireless power transmission system. This paper designs a TUBE model predictive control (TMPC) to make the system converge to the desired value along the reference trajectory. To reduce the difficulty of solving the reference trajectory, the idea of segmented optimization is adopted. State feedback is presented to further enhance the system’s anti-disturbance ability to increase the system transmission power. Moreover, the compensation for the sampling delay of the controller is proposed to ensure the stability of the control system. Experimental results demonstrate that, compared with traditional predictive control, the proposed TUBE MPC based on state feedback control strategy can increase the transmission power of the system by about 10% under uncertain misalignment.

In this paper, a fractional-order new generation of nk ± m-order harmonic repetitive controller (FO-NG-nk ± m RC), composed of a proposed new generation of nk ± m RC (NG-nk ± m RC) and a Taylor Series expansion based fractional delay (FD) filter using Lagrange interpolation with Farrow structure, is proposed. Compared with conventional nk ± m RC, NG-nk ± m RC has more advantages while achieving the same performance as the conventional nk ±m RC. Different FD approximation algorithms are compared for the first time from the perspective of controller computational burden. The inner relationship between Taylor Series expansion method and Farrow structure FD filter is explained, detailed mathematical derivation is provided, and a complete set of FD filter design methods is formed. When the fundamental frequency is not constant, the performance of nk ± m RC to track or eliminate any specific nk ± m-order harmonics will be seriously degraded. However, without changing the sampling rate, the proposed FO-NG-nk ± m RC can be used to improve the frequency adaptive performance. What's more, FO-NG-nk ±m RC provides a unified framework for integer/fractional-order nk ± m RCs. Experimental results of FO-NG-nk ± m RC controlled three-phase PWM inverter system show the effectiveness and advantages of the proposed FO-NG-nk ± m RC scheme.

In the context of Industry 4.0, it is necessary to meet customization manufacturing demands on a timely basis. Based on the related concepts of Industry 4.0, this paper intends to introduce mobile services and cloud computing technology into the intelligent manufacturing environment. A customization manufacturing system is designed to meet the demands of personalization requests and flexible production mechanisms. This system consists of three layers, namely, a manufacturing device layer, cloud service system layer, and mobile service layer. The manufacturing device layer forms the production platform. This platform is composed of a number of physical devices, such as a flexible conveyor belt, industrial robots, and corresponding sensors. The physical devices are connected to the cloud via the support of a wireless module. In the cloud, the manufacturing big data are processed, and the optimization decision-making mechanism pertaining to customization manufacturing is formed. Then, mobile services running in a mobile terminal are used to receive orders from customers and to inquire the necessary production information. To verify the feasibility of the proposed customization manufacturing system, we also established a customizable candy production system.

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.