Passivity-Based Model Predictive Control of Three-Level Inverter-Fed Induction Motor

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.

Finite Control Set-Model Predictive Speed Control for Induction Motors with Optimal Duration

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 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.

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.

Finite control set model predictive control (FCS‐MPC) has been widely used in the control of grid‐connected converters with the advantages of fast dynamics, multi‐objective control, and easy implement. However, the conventional FCS‐MPC bears with variable switching frequency, high current ripple and computational burden. An improved current model predictive with the cost function‐based modulation scheme (CFM‐MPC) is proposed for a three‐phase three‐level VIENNA rectifier to improve the power quality. First, the mathematical model and voltage vector are given according to the principle of deadbeat control. Then, the voltage vector of different voltage vectors are selected according to the location of the voltage vector reference, and the switching action time of the selected are directly calculated by the inversely proportional with cost function value of the selected vectors. It remains the merits of both the conventional MPC and space vector pulse width modulation schemes to track the optimum voltage vector without increasing the computational burden. Finally, a comparative study with the proposed CFM‐MPC and conventional FCS‐MPC has been conducted to verify the superiority of the proposed scheme. The results show the proposed CFM‐MPC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion and neutral point potential balance.

This paper presents a novel hybrid model predictive multilayer control (HMPMC) strategy for modular multilevel converters (M2LCs). The proposed hybrid strategy uses a modified model predictive direct slope control (MPDSC) concept to regulate the load current while concurrently minimizing the switching frequency, circulating current, and capacitor voltage variations through a new MPMC concept. The multilayer philosophy uses dedicated control layers to independently evaluate its control variables. As the main advantage, the hybrid strategy enables operation of M2LC at low switching frequencies. Moreover, this paper proposes a hierarchical screening algorithm, which estimates the minimum number of switching states required to be evaluated for each control variable, reducing the computational burden. An execution order for each control layer, in which the variables are assigned to control layers as appropriate, is also incorporated to further reduce the computational burden. Instead of using conventional weighting factors, this paper uses tuning factors for each layer, which is based on the number of switching states of the lowest cost to make the tuning process efficient and adaptable to any operating conditions. To demonstrate the validity, a prototype single-phase, five-level, 380-VA M2LC is designed, built, and controlled through the proposed strategies. The performance of prototype is investigated under both the steady-state and transient conditions, and the experimental results are presented in comparison with simulations and benchmarked against both the conventional finite control set model predictive control (FCS-MPC) and an improved MPC strategy. The results clearly indicate that the proposed strategy enables efficient operation of the converter at both significantly lower switching frequency and reduced computational burden, which is preferred for high-power applications.

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.

This article proposes a new finite control set model predictive control (FCS-MPC) strategy that simultaneously evaluates two targeting control objectives including speed and currents in a single cost function, achieving high-performance single-closed-loop control structure. Besides, aiming at the calculation delay problem of the model predictive control controllers, a novel calculation delay compensation method by predicting the current variation within the delay time is proposed. In this article, an improved machine model that is especially designed for the multiobjective FCS-MPC operation is illustrated at first. Then, a new cost function that can evaluate the tracking performance of speed and d-axis current and the steady-state performance of the q-axis current is developed. Compared with the conventional FCS-MPC approaches, extra speed controllers are not needed so that the proposed control topology becomes simpler. Then, in order to tune the weighting factors for the speed and currents in the cost function, an efficient handling strategy containing two implementation procedures, state variable normalization and balance of state sensitivity to voltage alteration, is developed. Finally, a brand-new computation delay estimation and compensation technique based on the dual sampling within a control period is proposed to reduce the current and torque ripples during the control process. Comparative simulation and experiments conducted on a three-phase 1.5-kW permanent magnet synchronous machine drive system are employed to verify the effectiveness of the proposed techniques.

This article proposes a phase-shifted model predictive control (PS-MPC) strategy to fully exploit the most relevant features of finite control set model predictive control (FCS-MPC) and phase-shifted pulse width modulation (PS-PWM) in a three-level active neutral point clamped (3L-ANPC) converter. Conventional PS-MPC strategies utilize a steady-state control input reference, typically a harmonic-free signal depending on the converter topology. However, this article demonstrates that the PS-MPC strategy applied to the 3L-ANPCconverter requires a control input containing a third-harmonic component with a specific magnitude and phase angle, which improves output current tracking and capacitor voltage balancing. By designing synchronized models for each carrier, the predictive controller optimizes duty cycles independently, ensuring the effective regulation of both control variables while addressing model nonlinearities. The proposed strategy is experimentally validated using a DSP F28379D microcontroller and a three-phase 3L-ANPCconverter using a total of 64 switching states in a grid-connected application. Compared to PS-PWM and FCS-MPC, the proposed approach maintains a fixed switching frequency, improves steady-state tracking, and enhances capacitor voltage balancing with a fast dynamic response. Additionally, it outperforms PS-MPC with control input change penalization (PS-MPC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol\Delta} \boldsymbol{u}$</tex-math></inline-formula>) by reducing spectral dispersion while achieving lower execution time than both FCS-MPC and PS-MPC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol\Delta} \boldsymbol{u}$</tex-math></inline-formula>, making it more suitable for real-time implementation. Such results are also discussed with a video demonstrating the real-time implementation.

The DC-DC converter represents a crucial component in renewable energy sources. The stability and dynamic capability enhancement of the DC/DC converter have emerged as a significant research topic in the current era. Model predictive control (MPC) is particularly prevalent due to its high dynamic response speed, simplicity of the controller design, and capacity for multi-objective optimization. However, the traditional finite control set model predictive control (FCS-MPC) method suffers as a result of variable switching frequency and vast computing. To improve the dynamic performance of the converter, a novel nonlinear control strategy named fixed switching frequency MPC and passivity-based control (PBC), named FSF-PBMPC, are both proposed. They could allow to achieve fixed switching frequency and to enhance the system’s dynamic response speed. Firstly, the Euler-Lagrange (EL) model of the boost converter is established. Secondly, the relationship between duty cycle and MPC is established. Ultimately, the output voltage of PBC is incorporated into the cost function of the FCS-MPC. The characteristics of PBC power shaping and damping injection can enhance the system’s immunity to interference, improve the system’s dynamic response speed, and thus reinforce the system’s stability. Then, depending on MATLAB, the simulation results can prove that the proposed strategy has the effect we expected.

Three‐phase induction motor (IM) drives play an important role in the variable speed applications from fractional horsepower to multi Megawatt power; it controls and evaluates them to be an interesting subject. This study presents a novel model‐predictive direct torque control (MPDTC) for IM drives. It combines the direct torque control (DTC) and finite control set model‐predictive control (FCS‐MPC) methods, and uses the predictive look‐up tables to enhance the overall performance of IM as the good steady‐state performance, lower switching frequency, and to achieve relatively low computational complexity. The simulations and experiments are performed to verify the effectiveness of the proposed control strategy. As the main conclusion of this work, it overcomes the disadvantages of the DTC method such as the high torque and stator flux ripples and those of the FCS‐MPC method such as the large amount of calculation and high average switching frequency. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

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.

An improved finite control-set model predictive control for nested neutral point-clamped converters under both balanced and unbalanced grid conditions

As an open-loop model predictive control algorithm, finite control set model predictive control (FCS-MPC) scheme in power converter system is based on assumption that responses of optimal control implemented on prediction model agree well with actual system. The influence of model parameter mismatches and environment disturbance on control performance of scheme is neglected. Then, based on feedback correction strategy in traditional model predictive control algorithm, we derive a finite control set model predictive control with feedback correction scheme (FCS-MPCFC) that allows us to adjust prediction model output at current instant by model prediction error at previous instant, and the closed-loop correction of prediction model output is achieved. Simulations comparison analyses on a two-level three-phase inverter with multi-type model parameter mismatches controlled by traditional and improved FCS-MPC scheme are presented. Experiments are carried out on DSP controller platform.