Sequential Model Predictive Control of Stand-Alone Voltage Source Inverters

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 work presents a Sequential Model Predictive Voltage Control of packed u cell (PUC) for grid connected applications. PUC inverter has become a research of interest due to the simultaneous advantages of flying capacitor and cascaded H bridge with less component count. Grid connected PUC provides the challenge of simultaneous control of link voltage and load current. Model Predictive Control provides superior multi objective, multi constraint handling and simplicity in implementation. The principle of model predictive control is about minimizing the cost function to get an optimized switching vector for every sample. Sequential model predictive control is a weight factor less algorithm to simultaneously control any number of control variables. However, the performance highly depends on the priority and the switch redundancy of the power converter. Performance analysis of the sequential MPC under various operating conditions has been presented for a PUC inverter. MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> / SimPowerSystems has been used to perform the simulations and performance has been compared with the existing weight factors based model predictive control algorithm.

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 typical finite-set model predictive control (FS-MPC) scheme for LC-filtered voltage source inverters (VSIs) lack inertia and damping, and the output current is easy to exceed the limit under the load-step change. Meanwhile, since the current sensor is needed to measure current information, the hardware cost is increased. To resolve these problems, an improved model predictive control combined with a virtual synchronous generator (MPC-VSG) is proposed in the paper. Above all, an MPC with a multi-objective cost function (CF) is used to minimize the tracking voltage error while providing overcurrent protection. Then, to provide the inertia and damping needed by the system, a VSG scheme is employed to generate voltage reference. Besides, a Luenberger observer is utilized to observe the inductor current. In this way, the number of current sensors is reduced to save the hardware cost. Finally, some comparative simulation experiments in Simulink verify the effectiveness of the improved MPC-VSG.

This paper proposes an event-triggered variable-sequential model predictive control (ET-VSMPC) to address the issues of high switching frequency and cumbersome weighting factor adjustment in conventional model predictive control (CMPC). The proposed ET-VSMPC has three key features: First, a novel variable-sequential model predictive control (VSMPC) that dynamically adjusts the optimization sequence of control objectives is proposed, thereby resolving the suboptimal issues caused by the fixed control sequence in traditional sequential model predictive control (SMPC). Second, an event-triggered mechanism is incorporated into VSMPC, which triggers control updates only when system states deviate beyond preset thresholds. This significantly reduces the average switching frequency while maintaining robust control performance. Third, ET-VSMPC relaxes the constraints on control objectives through event-triggered conditions, overcoming the limitation of fixed candidate vector numbers in SMPC. This enhances the algorithm's flexibility and adaptability. Finally, both simulation and experimental results validate that the proposed ET-VSMPC exhibits excellent steady-state and dynamic performance. Under identical switching frequencies, ET-VSMPC achieves superior current and voltage control performance compared to CMPC, while improving computational efficiency by 36%.

Conventional finite control set model predictive direct power control for grid-connected inverter causes high current harmonics and power fluctuations, during per control cycle, only one voltage vector is used. To address this issue, this paper introduces a hybrid vector model predictive direct power control (HV-MPDPC) strategy. A power prediction model is established in accordance with the instantaneous power theory. Then, eighteen alternative hybrid voltage vector combinations are formed by integrating double-vector and triple-vector combinations. Through cost function-driven selection of the optimal voltage vector combination, the power control performance and power quality are enhanced. The proposed strategy's effectiveness is validated by simulation results.

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.

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 the finite control set model predictive control (FCS-MPC) strategy of the grid-tied inverter, the current ripple (CR) affects the selection of optimal voltage vectors, which leads to the increase of output current ripples. In order to solve this problem, this paper proposes a CR reduction method based on reference current compensation (RCC) for the FCS-MPC strategy of grid-tied inverters. Firstly, the influence of the CR on optimal voltage vector selection is analyzed. The conventional CR prediction method is improved, which uses inverter output voltage and grid voltage to calculate current ripples based on the space state equation. It makes up for the shortcomings that the conventional CR prediction method cannot predict in some switching states. The improved CR method is more suitable for the FCS-MPC strategy. In addition, the differences between the two cost functions are compared through visual analysis. It is found that the sensitivity of the square cost function to small errors is better than that of the absolute value function. Finally, the predicted CR is used to compensate the reference current. The compensated reference current is substituted into the square cost function to reduce the CR. The experimental results show that the proposed method reduces the CR by 47.3%. The total harmonic distortion (THD) of output current is reduced from 3.86% to 2.96%.

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.

Finite control set model predictive control (FCS-MPC) of three-level neutral point clamped inverter-fed induction motor has received wide concern recently, thanks to its low switching frequency and fast dynamics. However, the performance largely depends on the accurate measurement and parameters which determine the feedback and control loops, respectively. Besides, FCS-MPC demands a large number of resources to perform the exhaustive search, which indicates that the computation burden increases exponentially with the increasing switching states. Aiming at improving the robustness under the condition of unavoidable measuring noises and parameter variation as well as reducing the computational burden, a passivity-based model predictive control (PB-MPC) scheme is presented in this article. For an ideal system, the PB-MPC has a similar character as that of the FCS-MPC. In real applications, where noises and disturbances impact the system more or less, PB-MPC outperforms FCS-MPC due to the power shaping and damping injection inherited from passivity-based control which ensures the asymptotic stability. A Lyapunov function is designed to prove the stability of the proposed scheme. The stability and efficiency are verified by both simulation and experimental results.

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.

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

The research on grid-connected inverters (GCI) is gaining momentum as more and more focus is given to clean energy sources like solar photovoltaic (PV) and wind energy. Conventional GCI uses the proportional-integral (PI) controller in conjunction with the sinusoidal pulse width modulation (SPWM) technique for pumping power to the grid. Model predictive control (MPC) is becoming more relevant in the field of power electronics. Conventional MPC uses a single prediction horizon or single step. This paper proposes the estimation of the optimization criterion for more than one prediction horizon, termed a multistep MPC. The multistep MPC is an extension of the basic MPC. The MATLAB/Simulink simulations of the proposed multistep MPC and the conventional single step MPC are presented in this paper. The total harmonic distortion (THD) values are compared between the conventional MPC and multistep MPC for varied operating conditions. The settling time of the controllers as well as the deviations in the cost function are compared. The simulation results indicate a better performance of the multistep MPC in comparison with a conventional single horizon MPC.