A modulated model predictive control scheme for a two-level voltage source inverter

Model predictive control (MPC) presents important advantages in the control of the power converter and drives such as, fast dynamic response and capability to include nonlinear constrains. These have positioned MPC as a powerful and realistic control strategy, however, it also has disadvantages such as variable switching frequency and parameter sensitivity. This paper applied a modulated model predictive speed control that guarantees a fix switching frequency and, thanks to disturbance compensation, robustness to parameters variation. The strategy is validated and compared to finite set model predictive speed control through simulation results.

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 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.

The main drawbacks of traditional finite set model predictive control are high computational load, large torque ripple, and variable switching frequency. A less complex deadbeat (DB) model predictive current control (MPCC) with improved space vector pulse-width modulation (SVPWM) under a single-phase open-circuit fault is proposed. The proposed method predicts the reference voltage vector in the α-β subspace by employing the deadbeat control principle on the machine predictive model; thus, the exhaustive exploration procedure is avoided to relieve the computational load. To perform the constant switching frequency operation and achieve better steady-state performance, a modified SVPWM strategy is developed with the same conventional structure, which modulates the reference voltage vector. This new approach is based on a redesigned and adjusted post-fault virtual voltage vector space distribution that eliminates the y-axis harmonic components in the x-y subspace and ensures the generation of symmetrical PWM pulses. Meanwhile, the combined merits of the DB, MPCC, and SVPWM methods are realized. To verify the effectiveness of the proposed control scheme, comparative experiments are performed on a five-phase permanent magnet synchronous motor (PMSM) drive system.

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.

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.

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.

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

Two-level voltage source inverters (2L-VSIs) with output LC filters are widely used in uninterruptible power supplies, distributed power generation, and microgrids. This paper proposes a novel sequential modulated model predictive voltage control scheme to control the load voltages and inverter currents of the 2L-VSI in a decoupled manner without the need for weighting factors. In this proposed scheme, two cost functions, each targeting the load voltages and inverter currents of the 2L-VSI, are deployed sequentially, rather than integrated into a dual-objective cost function. This eliminates the need for a weighting factor to balance the two control objectives while retaining the benefits of a modulated model predictive voltage control scheme such as constant switching frequency, fast transient response, lower total harmonic distortion, and reduced steady-state error. The performance of the proposed control scheme is validated through MATLAB simulations on a 10 kW system with linear and nonlinear loads and compared with the classical modulated model predictive voltage control.

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.

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.

Finite control set model predictive control (FCS-MPC) has been widely studied and applied to the power converters and motor drives. It provides the power electronics system with fast dynamic response, nonlinear system formulation, and flexible objectives and constraints integration. However, its variable switching frequency feature also induces severe concerns on the power loss, the thermal profile, and the filter design. Stemming from these concerns, this article investigates the variable switching frequency characteristics of FCS-MPC on the grid-connected inverters. An intuitive relationship between the switching frequency and the magnitude of the converter output voltage is proposed through the geometry analysis, where the switching frequency is maximized when the converter output voltage is around one-third of the DC bus voltage and decreasing when the output voltage moves away from this value. The impacts of this variable switching frequency property on the power loss and current harmonics are also analyzed. Simulation and experimental results both verify the proposed property. With this intrinsic property, FCS-MPC can autonomously achieve a less-varying temperature profile of power modules and an improved reliability compared with the conventional control strategy.

Classic finite-set model predictive control techniques are distinguished by a variable switching frequency which causes noise, large voltage and current ripple. This paper presents an enhanced predictive current control technique with fixed switching frequency applied to the six-phase drives and a Kalman Filter estimator. Simulation results are provided to show the efficiency of the current control algorithm using the mean square error and total harmonic distortion as a figures of merit, thus concluding that the system can work properly with this enhanced technique.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.