There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC.

In this paper, two continuous control set model predictive control (CCS-MPC) methods with one-step prediction horizon for three-level flying capacitor boost converter (FCBC) that can provide constant switching frequency are presented. The first one is explicit model predictive control (EMPC), which can provide explicit solution. In order to further reduce the complexity and the computational burden of EMPC while meeting the control requirements, a simple model predictive control (SMPC) is proposed as the second CCS-MPC method. The proposed CCS-MPC will be used as the inner loop controller to control the inductor current and flying capacitor voltage. Besides, a suitable outer loop control strategy for the output voltage is proposed to provide a suitable inductor current reference to improve the dynamic response of the output voltage while avoiding overcurrent. Compared with the previous control method for three-level FCBC, the proposed control method has good dynamic performance and stronger ability to resist input voltage disturbance. The effectiveness of the proposed method is verified by simulation and experimental results.

Cascaded boost-buck PFC (CBBPFC) converters offer a wide voltage conversion ratio and a near-unity power factor but require a large output electrolytic capacitor, leading to poor reliability and power density. In this paper, a coordinated two-stage operation and control strategy is proposed to significantly minimize the capacitor requirement without any other hardware changes. In a conventional CBBPFC converter, the boost and buck stages either operate independently nor complementally. In contrast, the proposed method operates the two stages in a concerted manner, so it is possible to use the dc-link capacitor with certain voltage fluctuation to buffer the power imbalance between the AC input and DC output. A new coordinated control strategy and a fluctuation-ratio based design consideration are developed to coordinate the operation of the two stages, further complement the system design. A 200W CBBPFC prototype based on the design concept exhibits a maximum reduction of the output capacitor by 83%, a peak efficiency of 95.8%, and a power factor of 0.99.

Prompt detection of power switch failure is crucial for fault-tolerant operation of multilevel converters. This article presents a new technique of detecting, identifying, and locating a device fault in a multilevel dc-dc flying capacitor (FC) buck converter (FCBC). This is realized by continuously analyzing the magnetic component (inductor or transformer) voltage harmonics for failure detection and subsequently identifying failure type and location with a look up table method. The harmonics analysis is performed by the existing FPGA controller using discrete Fourier transform (DFT). This new approach uses an auxiliary winding in the existing inductor core without adding any new hardware and can identify the location and fault type with good accuracy and speed. The experimental results validate the proposed concept on a three-level GaN-based FCBC prototype.

SiC MOSFETs are increasingly used in various power electronic converters due to their low switching losses, high operating frequency and high power applicability. The crosstalk phenomenon which will lead shoot-through in bridgeleg configuration and overvoltage breakdown of the device is more serious due to the high switching speed characteristics. In this paper, a smart self-driving crosstalk suppression gate driver is proposed based on the resistor-capacitor-diode (RCD) level shifter circuit for SiC MOSFETs, in which a low impedance branch of Miller clamping circuit is provided for the crosstalk current. In addition, no extra control signal is required which does not increase the complexity of the driver circuit. Then, by modeling the equivalent circuit of the proposed gate driver, the quantization relationship between the clamping capacitor and the crosstalk voltage is derived. Finally, a double-pulse test platform is built to verify the effectiveness of the proposed gate driver under different clamping capacitors, input voltage and load current conditions. The experimental results show that the proposed gate driver can suppress the crosstalk voltage effectively. Meanwhile, the switching loss is reduced by a maximum of 16.6

The article proposes a new vector analysis based model predictive control (VAMPC) method for multilevel converters with four different switch configurations. It is based on track of an error vector using fundamental state vectors, and can achieve deadbeat control of four switching states multilevel converters with multiple control objectives. Compared with conventional finite control set model predictive control (FCS-MPC) method, VAMPC can achieve excellent spectrum characteristics in the case of a slight increase in computational burden. The VAMPC method is experimentally validated on a 100-kHz GaN-based three-level flying capacitor (FC) converter. Compared with the FCS-MPC, VAMPC leads to significant reduction in steady-state control errors. For example, the inductor current total harmonic distortion of the FC converter is now reduced by more than 60%.