Cell-by-Cell-Based Finite-Control-Set Model Predictive Control for a Single-Phase Cascaded H-Bridge Rectifier

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

Vienna rectifier, which is a nongenerative-boost-type rectifier, is widely employed in electric vehicle charging systems. Nevertheless, the Vienna rectifier faces the challenge of current distortion elimination, current ripple mitigation, and neutral-point (NP) voltage balance. As these problems are mutually coupled, the conventional finite control set model predictive control (FCS-MPC) methods cannot solve these problems properly. To address these issues, a three-layer double-vector FCS-MPC method is proposed. In the first layer of the proposed FCS-MPC method, the sectors are divided and the candidate vectors are selected to reduce the current distortion. In the second layer of the proposed FCS-MPC method, the double vectors with optimal duty cycles are adopted to reduce the current ripples. In the third layer of the proposed FCS-MPC method, the NP voltage is balanced by selecting the redundant p-type small vector or n-type small vector. Through the proposed method, the current distortion elimination, current ripple mitigation, and NP voltage balancing are realized at the same time. The effectiveness of the proposed method has been verified by simulation and experiment.

This work introduces the control concept of variable sampling time finite control-set model predictive control (FCS-MPC). The new control concept is introduced in theory based on a review of the conventional FCS-MPC concepts performed with a constant sampling time. Based on the partitioning of the sampling instant in multiple smaller sampling instants it is possible to optimize, besides the switching states, the switching states turn-on times. Therefore, the proposed variable sampling time FCS-MPC sets both: the switching state and the related turn-on times. To utilize the available calculation power for longer sampling instants an adaptation of the control- and prediction horizon to the sampling time is proposed. The theoretical control concepts are applied to the control of a grid connected two-level voltage-source converter where a simple L-type line-filter is used to demonstrate the control performance of the variable sampling time FCS-MPC algorithms in the laboratory environment.

This paper presents an investigation of the finite-control-set model predictive control (FCS-MPC) of a five-phase induction motor drive. Specifically, performance with regard to different selections of inverter switching states is investigated. The motor is operated under rotor flux orientation, and both flux/torque producing (d-q) and nonflux/torque producing (x-y) currents are included into the quadratic cost function. The performance is evaluated on the basis of the primary plane, secondary plane, and phase (average) current ripples, across the full inverter's linear operating region under constant flux-torque operation. A secondary plane current ripple weighting factor is added in the cost function, and its impact on all the studied schemes is evaluated. Guidelines for the best switching state set and weighting factor selections are thus established. All the considerations are accompanied with both simulation and experimental results, which are further compared with the steady-state and transient performance of a proportional-integral pulsewidth modulation (PI-PWM)-based current control scheme. While a better transient performance is obtained with FCS-MPC, steady-state performance is always superior with PI-PWM control. It is argued that this is inevitable in multiphase drives in general, due to the existence of nonflux/torque producing current components.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

This paper proposes a model predictive control based on three voltage vectors for an interior permanent-magnet synchronous motor in a rotating reference frame. The proposed method can obtain the reference voltage vector quickly by predicting only one voltage vector during one sampling period using the characteristics of the deadbeat control. In addition, to obtain better steady-state performance, the optimal vector duration ratios can be obtained from the cost function to minimize the error between the predicted reference voltage vector and the synthesis vector by using the switching sequence relationship of a symmetrical three vector, which is different from applying a single voltage vector in the conventional finite-control-set model predictive control (FCS-MPC). To verify the proposed method, the experimental results are compared with the conventional FCS-MPC and two-vector-based MPC.

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

This Paper evaluates the integration of fuel cell power system with the grid using finite control set model predictive controller. A mathematical model of the phosphoric acid fuel cell power system is designed. The grid and the fuel cell are modeled in PSCAD simulation environment. Direct power control technique for model predictive controller is used to control the converter. The steady state results are shown in the paper, Also, a single phase fault and three phase fault is applied at the grid side to study the effect of integrating the fuel cell with the grid. The simulation results show model predictive controller can effectively integrate fuel cell with the grid.

This paper deals with a particular problem in the conventional finite set model predictive control (FS-MPC) method in VSI drives. When FS-MPC is experimentally implemented, the controller chooses any one of the eight available voltage vectors. Therefore, if the controller selects any of the far states, simultaneous switching events among the inverter legs occur. This causes line-line voltage polarity reversal (LLVPR) with the voltage change twice the value of the DC-link voltage. The fast LLVPR combined with the reflected wave by the motor cable can damage the insulation of the stator winding. In this letter, the problem of LLVPR in electric drives with FS-MPC is described, and the solution to this issue is proposed. Experimental results to validate the proposed method are shown.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

This paper evaluates the performance of strategies based on finite-control-set model predictive control (FCS-MPC) aimed at reducing or fixing the converter switching frequency or decreasing the spread of the harmonic spectrum in multilevel hybrid cascade converters (HCCs). These properties are desirable for medium- to high-voltage applications, where minimizing switching losses is crucial, as well as for applications employing passive filters, where resonance modes can be excited. The strategies evaluated are input restriction, notch filtering, period control, and PWM restriction. Key aspects considered in this work are (i) the evaluation of the steady-state and transient performance of FCS-MPC strategies proposed for two-level converters in a multilevel topology, and (ii) the evaluation of the computational cost associated with the implementation of these strategies on a multilevel converter with a high number of available inputs. As a typical application, the study is carried out employing a five-level HCC experimental prototype driving an induction motor through indirect vector control. To perform a fair comparison between the strategies, a control platform based on a cost-effective Zynq system on chip is proposed, which allows for achieving the hard timing constraints imposed by FCS-MPC strategies. The results show that the PWM restriction strategy achieves the best steady-state performance among the evaluated strategies, with an error 400 times smaller than that of the second-best strategy (input restriction), with an average switching frequency of 962.5 Hz, which differs from the desired average frequency by 3%, and a maximum difference in power distribution between modules of 0.8%. In addition, the system-on-chip hardware achieves a competitive execution time of 46 μs when the ARM Cortex solution is implemented and 20 μs when the ARM Cortex–FPGA solution is used instead, employing the 512 inputs available in the FCS-MPC algorithm. The studies, performed in steady-state and transient regimes, confirm (i) the feasibility of the evaluated algorithms in an HCC topology and (ii) the feasibility of the control platform for implementing high-computational-burden algorithms with a low sampling time.

In this paper, a Lyaupnov-based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The eight voltage vectors of the two-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the maximum torque per ampere operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the eight vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.

In this paper, a simplified finite‐control‐set model predictive control (FCS‐MPC) method with a deadbeat solution is proposed for eight‐switch, three‐phase, neutral‐point‐clamped converters (ESTP‐NPCs). Specifically, in order to improve the computational efficiency, a novel sector distribution scheme is presented by dividing the whole voltage vector space into eight sectors. In the each sector, only three voltage vectors are determined as new candidate finite control sets (FCSs), and then the optimal voltage vector that minimizes the predesigned cost function is selected from the new candidate FCS. Meanwhile, in order to avoid the selection of the weighting factor, a multiobjective optimization strategy based on an average ranking is introduced into the proposed strategy. In addition, the DC‐link capacitor voltage oscillations and deviations are suppressed effectively, and the time delay of control command is compensated. In view of these characteristics, the proposed control strategy (i) has lower computational burden, (ii) has good dynamic performance, (iii) does not need the tuning of weighting factor, and (iv) does not need the modulation method. Finally, compared to the conventional FCS‐MPC, the proposed method is more efficient as demonstrated by simulation results. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.