Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

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.

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.

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.

The traditional finite-control-set model predictive control (FCS-MPC) method for a cascaded H-bridge (CHB) rectifier has two main issues: heavy computational burden and low steady-state current performance. In this paper, a novel FCS-MPC method has been proposed for a single-phase CHB rectifier. The proposed method solves the optimization problem of FCS-MPC for one cell by one cell, like a “pipeline.” In the proposed method, the sampling period is divided into equal intervals by the number of cells. At the beginning of the first interval, the first cell selects its switching state to be applied. Then, the following cell selects its switching state to be applied at the beginning of next interval. Finally, the selected switching state of last cell will be applied at the beginning of the last interval. A cost function presenting the control objectives of common source current error and itself dc-link voltage error is evaluated for each cell. A single-phase three-cell CHB rectifier controlled by a DSpace DS1104 is tested and the experimental results show that a significant reduction in computational time, an improved steady-state current performance, and a comparable dynamic response are achieved in the proposed method in comparison with the traditional FCS-MPC method.

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

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.

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.

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

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 article aims to first focus on an improvement of finite control-set model predictive control strategy for power converters that is based on reinforcement learning event-triggered predictive control architecture with the help of adaptive dynamic programming technique and event-triggered mechanism subject to system uncertainties. Our development, endowed with the merits of reinforcement learning and event-triggered control as well as predictive control solution, is able to alleviate the issues of parametric uncertainties and high switching frequency inherent in the existing scheme, while retaining the merits of the finite control-set model predictive control. Finally, this proposal is experimentally evaluated, where robust performance tests confirm the interest and applicability of the proposed control methodology.

Induction motors are widely utilized in numerous applications due to their robustness, straightforward design, and affordability. Nevertheless, the precision of motor control can be affected by parameter mismatches and unmodeled system dynamics. Advanced control techniques like finite control set-model predictive control (FCS-MPC) have been designed to mitigate these issues. The FCS-MPC uses the system's mathematical model to predict future behavior of the system and select the most suitable control signal from a discrete set of options. Although FCS-MPC proves to be effective, traditional implementations can still face challenges from uncertainties such as parameter changes or external disturbances. This paper presents a novel robust predictive current control for induction motors in the stationary reference frame. It incorporates a discrete Luenberger observer to address the machine's parameter mismatches. A discrete-time voltage model is derived from the motor's dynamics, enabling the integration of a discrete Luenberger observer. This integration enhances controller performance, especially in the presence of parameter mismatches. In contrast to conventional FCS-MPC approaches, this method integrates stator voltage into the cost function instead of current, torque, or magnetic flux. Experimental results on an induction motor, tested under both steady-state and dynamic scenarios, confirm that this approach outperforms traditional predictive current control regarding robustness. The new method's enhanced control capability to better track the reference, even under significant variations in inductance and resistance, underscores its potential as an effective solution.

Finite control set-model predictive control (FCS-MPC) is a promising control scheme for power converters/inverters and electric drives due to its flexibility and simplicity. Using all the possible voltage vectors (VVs) generated by the inverter (7 VVs for 2-level inverter) and a discrete-time model of the system, the future behavior of the inverter can be predicted. Then, a certain cost function is employed for selecting the best switching action that will be applied in the next sampling interval (7 evaluations of the cost function are required). Accordingly, the main disadvantage of the traditional FCS-MPC scheme is the heavy calculation load. In this paper, an efficient FCS-MPC method for a grid-connected photovoltaic (PV) inverter is proposed in order to reduce the calculation burden of the traditional FCS-MPC. Based on the required reference current, the reference voltage vector (VV) is directly calculated. Then the sector where this reference VV is located is identified from its angle. Consequently, the cost function is evaluated only three times to obtain the optimal switching vector. The performance of the proposed efficient FCS-MPC is compared with the traditional FCS-MPC and voltage oriented control (VOC) schemes by simulation results for all operation conditions.

This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with merely grid-injected current sensors for an inductance-capacitance-inductance (LCL)-filtered grid-tied inverter, which can obtain a sinusoidal grid-injected current whether three-phase grid voltages are balanced or not. Compared with the conventional FCS-MPC method, four compositions are added in the proposed FCS-MPC algorithm, where the grid voltage observer (GVO) and Luenberger observer are combined together to achieve full status estimations (including grid voltage, capacitor voltage, inverter-side current, and grid-injected current), while the sequence extractor and the reference generator are applied to eliminate the double frequency ripples of the active or reactive power, or the negative sequence component (NSC) of the grid-injected current caused by the unbalanced grid voltage. Simulation model and experimental platform are established to verify the effectiveness of the proposed FCS-MPC strategy, with full status estimations under both balanced and unbalanced grid voltage conditions.

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.