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This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise. [ABSTRACT FROM AUTHOR]

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on predictive control in power converters and electrical drives. With the fast microcontrollers available today, applications of predictive control in power converters and electrical drives are a very powerful and attractive alternative to classical controllers. The use of predictive control offers a number of advantages: very intuitive approach, no need for linear controllers and modulators, easy inclusion of nonlinearities and restrictions, etc. However, predictive control schemes in power converters and electrical drives have not yet been implemented in industrial applications. Nevertheless, after some further progress, it can be expected that the advantages of predictive algorithms will lead to an increased number of industrial applications in the future. [ABSTRACT FROM PUBLISHER]

The papers in this special section on predictive control in power converters and electrical drives focuses on the following topics: 1) predictive control applied to power converters: ac/dc, ac/ac, dc/ac, ac/dc/ac, multilevel converters, etc.; 2) predictive control applied to motor drives: induction machines, permanent-magnet synchronous motors (PMSMs), etc.; and 3) design and implementation issues of predictive control: cost function selection, weighting factor design, delay compensation, model parameters errors, variable switching frequency, etc.; 4) applications of predictive control in industrial electronics. [ABSTRACT FROM PUBLISHER]

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter. [ABSTRACT FROM AUTHOR]

When control schemes based on finite control set model predictive control are experimentally implemented, a large amount of calculations is required, introducing a considerable time delay in the actuation. This delay can deteriorate the performance of the system if not considered in the design of the controller. In this paper, the problem is described, and the solution to this issue is clearly explained using a three-phase inverter as an example. Experimental results to validate this solution are shown. [ABSTRACT FROM PUBLISHER]

To overcome the shortcomings of the conventional continuous control set model predictive current control (CCS-MPCC), such as large overshoot and poor robustness, an extended surface-mounted permanent magnet synchronous motor (SPMSM) model-based multistep error tracking CCS-MPCC (MSET-CCSMPCC) is proposed in this article. First, a traditional CCS-MPCC is derived based on the conventional SPMSM model and its robustness is analyzed by considering the parameter mismatches. Second, an extended SPMSM model is given by incorporating the lumped disturbances into one disturbance part. Third, a sliding mode differentiator improved fast terminal sliding mode disturbance observer is designed to track the disturbances. Fourth, by compensating the extended SPMSM model for the estimated d- and q-axes disturbances, an extended SPMSM model-based CCS-MPCC (EXM-CCSMPCC) is designed. However, the EXM-CCSMPCC has serious step response overshoot. Fifth, an extended SPMSM model-based single step error tracking CCS-MPCC is presented, whose dynamic response and steady-state performances deteriorate when the overshoot is reduced. Finally, an MSET-CCSMPCC is proposed to reduce the overshoot and improve the robustness while maintaining excellent dynamic and steady-state performances. Experiments are implemented on a field-programmable gate array based hardware system to verify the excellent performances of the proposed method. [ABSTRACT FROM AUTHOR]

Predictive torque control (PTC) is based on the prediction of motor torque and stator flux for all possible voltage vectors of power converter. These predictions are derived from a mathematical model of the motor that depends on the motor parameters. But, these parameters may not match with their actual values. These parametric uncertainties lead to inaccurate predictions of torque and stator flux and affect the performance of the predictive algorithm. This paper presents a modified PTC for a permanent magnet synchronous motor to reduce the torque ripple at the presence of parametric uncertainty by improving the prediction accurateness. Simulation and experimental results are provided to show the effect of the proposed method. [ABSTRACT FROM PUBLISHER]

The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally. [ABSTRACT FROM AUTHOR]

Three-phase voltage source inverters (VSIs) with output $ LC$ filter are preferred topologies to provide voltage and frequency support for islanded ac microgrids. This article proposes a finite-set quasi-sliding model predictive control (FS-QSMPC) scheme for $LC$ -filtered VSIs to improve the output-voltage quality. By explicitly including a predictive sliding-mode function into the cost function, reduced steady-state ripple and enhanced robustness are achieved compared to typical FS-MPC. Besides, theoretical analysis of stability and robustness for FS-QSMPC is given. Analytical tuning of the weighting factor is also derived to decrease the tuning effort. Comparative simulations and experiments verify the presented approach. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An improved particle swarm optimization (PSO) algorithm based on Gaussian distribution model is proposed to realize the autotuning of weighting factors for the cost function design in the model predictive control method. First, the design principle of the weighting factors in model predictive torque control for permanent magnet synchronous motor system is analyzed. Then, using the root mean square of the current error in the two-phase rotating coordinate system and the system switching frequency as references, the objective function of the particles in the PSO is designed by considering the main control goals of reducing the torque ripple, the current total harmonic distortion, and the switching frequency. The Gaussian individual optimal distribution model is used to update the particle position on the structure of the conventional PSO algorithm. The experimental results show that the proposed method can solve the problem of weighting factors design as it reduces the switching frequency of the system while achieving excellent steady-state performance. [ABSTRACT FROM AUTHOR]

Recently, fault-tolerance control (FTC) methods have been proposed to further improve the reliability of T-type three-level converters. However, the application of model predictive control (MPC) in FTCs is rare, and the neutral point (NP) voltage imbalance before an open-switch fault occurs has not been solved. This article focuses on these two problems and proposes two FTC control methods: sequential MPC and sequential model predictive tolerance control. Both methods are implemented by designing a sequential predictive control that first considers the NP voltage balance and then grid current tracking. Through the staggered operation of the two methods, a T-type grid-connected converter with an LCL filter can operate normally after an open-switch fault occurs. Moreover, both methods can achieve the NP voltage balance and excellent grid current outputs, eliminate the time-consuming selection of weighting factors, and reduce the number of calculation loops compared with the traditional model predictive tolerance control, thereby improving the control flexibility. The performance and comparison of the open-switch FTC with respect to the NP voltage oscillations, total harmonic distortion of the grid current, and pole voltage were demonstrated using numerous simulation and experimental results. [ABSTRACT FROM AUTHOR]

This article proposes a continuous control set predictive speed control (CCS-PSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives, where the prediction horizon is short and the computational complexity is low. The optimization problem of the proposed CCS-PSC is formulated and analyzed. The current and voltage constraints are transformed into incremental voltage constraints, which can be updated in real-time. A reduced-order incremental model of SPMSM is adopted for the prediction, where integrators are naturally embedded. Hildreth’s quadratic programming algorithm is used to solve the optimization problem, where the active constraints are identified and the reference voltage is calculated. The performance of the proposed strategy is validated through comparative experiments with finite control set predictive speed control (FCS-PSC), cascaded CCS-PSC and field-oriented control. The results demonstrate the superb dynamic performance of the proposed CCS-PSC strategy, while the phase current total harmonic distortion is relatively low. [ABSTRACT FROM AUTHOR]

Direct model predictive control (DMPC) has become an effective control alternative for the grid-tied modular multilevel converter. However, classical DMPC of the grid-tied MMC relies on accurate system model. System parameter variations will deteriorate the control performances of power, grid current, and circulating current. Besides, the inherent fewer switching actions of DMPC will also lead to poor circulating current performance. To solve these issues, this work proposes a robust DMPC solution for the grid-tied MMC. Novelties lie in two aspects. First, a virtual-flux (VF) based online inductance estimator is proposed, which utilizes the VF estimation error caused by inductance deviation. It achieves fast and accurate estimation of AC equivalent inductance, and reduces the dependency on system parameters. Thereafter, we propose a hybrid predictive control frame with a simple proportional-resonant (PR) controller, which considerably suppresses the circulating current. Finally, experimental results verify the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed energy resources into islanded microgrids, a grid-forming inverter (GFI) has become the key element interfacing renewable energy sources. Usually, the GFI is employed with an output filter to minimize the harmonic content achieving high-quality output voltage regulation. To this end, model-predictive control (MPC) has been widely proposed to control the output voltage of GFI systems due to the high-quality performance, fast control response, and straightforward handling of constraints. However, an accurate model of the system is required for the conventional MPC to avoid a suboptimal performance under uncertainties. To overcome this known drawback, a novel model-free predictive control is proposed in this article. Consequently, the output voltage of an $LCL$ -filtered GFI is regulated without the knowledge of the physical model. [ABSTRACT FROM AUTHOR]

Standard model predictive control is an optimization-based control strategy that can handle multiple control objectives and system nonlinear constraints. However, it typically suffers from the limitation of the uncertainties in practical systems, such as external unknown disturbances and parametric uncertainties. Motivated by aforementioned limitation, in this article, a novel robust model predictive control framework, endowed with the merits of fuzzy logic system and finite control-set model predictive control solution, is proposed. The main objective of this article is to enhance the system robustness while guaranteeing adaptability to different conditions. More specifically, a fuzzy approximation point of view, which has a good potential to approximate the unknown nonlinear functions, is deployed and incorporated into the proposed design, which allows one to explicitly take the system nonlinear dynamics and uncertainties into account. The novelty of the proposed methodology relies on the fact that any prior knowledge and explicit information of system model parameters are not required, thereby resulting in considerable enhancement of robustness. Furthermore, the input-to-state stability of the approximation error system is proven through Lyapunov analysis, and it demonstrates that the estimated errors are uniformly ultimately bounded. Finally, the interest of the proposal is experimentally confirmed for modular multilevel converter. [ABSTRACT FROM AUTHOR]

Three-level T-type converters (3LT2C) with inductance–capacitance–inductance filters have high power quality of grid current, particularly in the low-voltage system. However, the switching loss and conduction losses degrade the overall efficiency of 3LT2C. The generated heat due to the losses endangers the reliability of the device and shortens its service life. To relieve the power losses as well as maintain high-quality current output, a low-loss-tolerant sequential model predictive control (LL-TSMPC) is proposed. First, based on the energy loss analysis under switching transitions per commutation, the prediction models of both switching loss and conduction loss are established. Second, the total loss reduction is considered as one control objective in LL-TSMPC. Finally, because multiobjective model predictive control (MPC) requires selecting appropriate weighting factors, complicating the design to solve this problem, TSMPC-based lexicographic optimization is introduced to eliminate the tradeoff factors and simplify the MPC controller design. The proposed method is tested on the hardware platform with the rated power of a 10-kW prototype. The experimental results show that the LL-TSMPC method can effectively reduce the switching losses while maintaining the high power quality of the grid current. [ABSTRACT FROM AUTHOR]

It is well known that predictive control methods can be affected by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties is a recurrent concern at the moment of evaluating the viability of this method for power electronics applications. This paper proposes an analytic approach to examine the influence of model parametric uncertainties on the prediction error of FCS-MPC for current control in a three-phase two-level inverter. The analysis shows that the prediction error is not only determined by parametric mismatch but also by the instantaneous values of load current and inverter output voltage. This implies that within each sampling period of the predictive algorithm several conditions of prediction error are generated, as multiple voltage vectors are evaluated. Simulation and experimental results are provided and discussed showing the effects of inaccuracies in the modeling of load resistance and inductance parameters on the performance of FCS-MPC. Even though steady-state performance is noticeably affected with parameter changes, especially when the load inductance is overestimated by the model, its fast transient step response is less affected by parameter changes. [ABSTRACT FROM PUBLISHER]

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC. [ABSTRACT FROM AUTHOR]

Aging and temperature changes in the passive components of an LCL-filter grid connected converter system (GCCs) may lead to parameter uncertainties, which can in turn influence its modeling accuracy for finite-control-set model predictive control (FCS-MPC). The presence of model errors will change the resonance point and deteriorate the power quality of the grid current, in turn degrading the active damping performance. In this situation, there is a serious possibility that the GCCs may malfunction and automatically disconnect from the grid, causing great challenges to the system stability. To solve this problem, first, prediction error analysis in FCS-MPC due to the model parameter errors is presented. Second, to achieve high accuracy and fast filter parameter estimation in utility, an adaptive online parameter identification method based on gradient descent optimization (GDO) has been proposed. Finally, to further reduce the searching time needed by the optimal iteration step, a variable iteration step searching method based on the root-mean-square-prop (RMSprop) GDO method is proposed. Experimental studies of an LCL-GCCs prototype in the laboratory have been conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An improved two-stage model predictive current control (MPCC) for three-phase three-level converters in the paper, which can simultaneously achieve common-mode voltage (CMV) reduction, effective dc-link capacitor voltage control, and decreased computational burden, is proposed. Compared to the other current control strategies, it has the capability of handling multiple control objectives and possesses a simple structure. Furthermore, it greatly reduces the CMV since the peak CMVs of all the candidate voltage vectors are constrained to be less than one sixth of the dc-link voltage. In addition, an optimal two-stage voltage vector selection mechanism is employed in the proposed method. It helps to significantly reduce the computational complexity. Simulation and experimental results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The extended state observer (ESO) has been found as an efficient solution for the model sensitivity in the model-predictive control (MPC). Generally, the ESO is used for disturbance rejection when the voltage reference is the output of the control law. The observed disturbance is subtracted from the control law. Thus, using disturbance rejection for finite-set MPC is a challenge when it is utilized by a cost function consisting of the errors of the torque and the flux. In this research, a modified disturbance rejection method is used in a feedforward shape to improve the robustness of the finite-set model-predictive torque control. In this regard, the motor parameters have not appeared in the torque prediction algorithm. Only a rough approximation of the motor parameters is needed to design the ESO, which means that the proposed method has a very low dependence on the motor parameters. Besides, a thorough tuning guideline is proposed to tune the local parameters based on the convergence analysis of the ESO by using the self-stable region approach and the Lyapunov function. The performance of the proposed MPC scheme is evaluated through simulations and experimental tests. The results of the proposed method are compared with the classic MPC results. [ABSTRACT FROM AUTHOR]

This article presents an optimal model predictive flux control (MPFC) for a two-level inverter fed induction motor. Integrating discrete SVM into FCS-MPFC enhances the performance of the IM drive. However, conventional DSVM-MPFC requires to enumerate and evaluate a higher number of virtual vectors in the prediction loop. In this paper, a high-efficient and low complexity voltage selection method is proposed to reduce the number of candidate voltage vectors from 38 to 15 without any suboptimality. Both steady-state and transient performances of the proposed method remain the same as the 38-vector based conventional DSVM-MPFC, producing the same cost-function values in all operating conditions. Furthermore, an online switching frequency reduction technique is proposed to achieve a minimum commutation per inverter vector change within each sampling cycle and between adjacent cycles. By appropriately arranging the sequence of real voltage vectors in each sampling cycle, a lower average switching frequency is achieved. The proposed switching frequency reduction method decreases the switching losses without compromising the performance of DSVM-MPFC as only the applied sequences of the real voltage vectors are optimized. Experimental studies are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The efficiency of the linear induction machine (LIM) is considerably low due to the large air-gap length and the additional losses resulting from the end-effects. However, so far, a little concentration has been done to increase the efficiency of this kind of motor. Therefore, the main objectives of this article are to eliminate the weighting factor, reduce the calculation steps, and increase the efficiency of the LIM aiding with a proposed finite-set model predictive direct flux control (FS-MPDFC), depending upon the maximum thrust per ampere (MTPA) strategy. The proposed FS-MPDFC is only based on the primary flux-linkage with the help of a novel cost function that does not require any weighting factors in order to considerably reduce the calculation steps. Moreover, the proposed FS-MPDFC is better than the model predictive current control in reducing the calculation step and decreasing the dependence on the transformation matrix and the machine parameters. In order to illustrate the effectiveness of the proposed MTPA-based FS-MPDFC, a comparison between the FS-MPDFC, with and without MTPA, and the previously suggested control techniques is presented and discussed. Moreover, a test bench platform of two 3-kW arc induction machines is constructed to emulate the drive performance of the actual LIM. Comprehensive simulation and experiments have fully demonstrated that the proposed FS-MPDFC with MTPA has lower calculation step, lower thrust ripple by 7%, and lower primary flux-linkage ripple by 0.4% compared to those of the FS-MPDTC. Moreover, the efficiency-based FS-MPDFC with MTPA is increased by 4% compared to that of the FS-MPDFC without MTPA. [ABSTRACT FROM AUTHOR]

Voltage source inverters with output LC filter enable a sinusoidal output voltage with low harmonics, suitable for islanded ac microgrid or uninterruptible power supply applications. Conventional finite-set model predictive voltage control (MPVC) applies only a single switching vector per control period, leading to a variable switching frequency and significant output ripple. This article resolves these issues by proposing an improved MPVC with optimal switching sequence (OSS-MPVC). First, an improved vector switching sequence is defined, aiming to reduce the output-voltage ripple with a constant switching frequency. Then, to tackle the difficulty in extending the OSS to high-order systems due to the coupling effect of the output filter, a generalized “one-step estimation” solution is proposed, which directly associates the control-variable gradients with the vector switching sequence. To further enhance the output-voltage tracking accuracy, intersample dynamics are taken into account in the cost function. The control delay and dead-time compensation are also considered. Simulations and experimental results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an efficient direct-model predictive control (EDMPC) technique for surface-mounted permanent-magnet synchronous generators (PMSGs) in variable-speed wind energy conversion systems. The proposed control technique is based on directly computing the reference voltage vector (VV) from the demanded currents using a deadbeat (DB) function. Then, a discrete-time integral action (DTIA) is added to this DB function to enhance the robustness of the proposed EDMPC scheme against variations of the machine parameters and to achieve a good steady-state response. The proposed DTIA is simple and easy to implement. Finally, according to the location of this reference VV, two evaluations of the quality function are only required. Accordingly, the proposed EDMPC technique with DTIA overcomes the following drawbacks of the conventional direct-model predictive control (DMPC): high calculation burden, sensitivity to parameters mismatches, and non-zero steady-state error. The performance of the proposed EDMPC technique with DTIA has been experimentally investigated and compared with that of the EDMPC with time delay control approach and with the performance of the convention DMPC using a $\text{14.5}\,\text{kW}$ PMSG. [ABSTRACT FROM AUTHOR]

One of the main drawbacks with the use of finite control set model predictive control (FCS-MPC) in power converters is the variable switching frequency. When not taken into consideration, it can vary randomly depending mainly on the operating point of the system and the sampling time. This will produce a wide distributed voltage and current spectrum, causing audible noise, resonances, and poor steady-state behavior. To address this issue, a new algorithm, based on the control of the switching period within the cost function, is proposed in this paper. The algorithm shows to be very effective in achieving a regular commutation pattern, similar to pulse width modulation (PWM) techniques, which is evidenced even by similarities in the sideband harmonics generated in the voltage spectrum. The algorithm is easy to design and implement, demanding very low computation power, and enables a combination of the advantages of FCS-MPC with the benefits of a PWM-like power quality. Simulations and experimental results are presented to validate the proposed method, which are compared to previously reported FCS-MPC techniques dealing with the same challenge. [ABSTRACT FROM PUBLISHER]

Direct model predictive control (DMPC) is a promising alternative for power electronics and electric drives. It takes the switching nonlinearity of the power converters and system constraints into consideration, without using an extra modulator. However, its one-switching-vector-per-control-interval character leads to big ripples of the control variables. Therefore, with a similar sampling frequency, its steady-state performance is not satisfying. In this paper, we propose a multiple-vector direct model predictive power control (MV-DMPPC) concept for the grid-side power converter control of a back-to-back converter permanent-magnet synchronous generator wind turbine system, using a fully field programmable gate array based solution. The proposed control scheme is compared with the classical DMPPC and two recently reported DMPPC scheme with duty cycle optimizations. Both simulation and experimental tests validate that the control performances are evidently improved with the proposed MV-DMPPC solution. [ABSTRACT FROM AUTHOR]

The predictive control method deals with the prediction of the motor behavior based on a mathematical model of the motor. This model is dependent on the motor parameters. However, these parameters may not match with their actual values due to the measurement error or they may change during the operation of the motor. All these uncertainties and model inexactitude lead to inaccurate prediction of the motor behavior and deteriorate the performance of the predictive algorithm. This paper presents an extension of the predictive current control (PCC) method to improve the prediction accuracy for a permanent-magnet synchronous motor (PMSM). The proposed strategy not only reduces the current ripple but also improves the robustness of the system against parameter uncertainties. Simulation and experimental results that confirm the good performance of the proposed method are presented. [ABSTRACT FROM PUBLISHER]

In this paper, the experimental validation of a predictive current control strategy for a four-leg indirect matrix converter is presented. The four-leg indirect matrix converter can supply energy to an unbalanced three-phase load while providing a path for the zero sequence load. The predictive current control technique is based on the optimal selection among the valid switching states of the converter by evaluating a cost function, resulting in a simple approach without the necessity for modulators. Furthermore, zero dc-link current commutation is achieved by synchronizing the state changes in the input stage with the application of a zero-voltage space vector in the inverter stage. Simulation results are presented and the strategy is experimentally validated using a laboratory prototype. [ABSTRACT FROM AUTHOR]

The finite control state predictive torque control (FCS-PTC) method is known to produce a fast response. However, when compared with the direct torque control (DTC) method, which is inherently encoderless, the FCS-PTC method has a disadvantage because of its speed dependence. An encoderless FCS-PTC method that is based on a revised prediction model for an induction machine is proposed and experimentally verified in this paper. To observe the unmeasured variables, an encoderless full-order sliding-mode observer (EFOSMO) is applied. Neither the revised prediction model nor the EFOSMO uses the estimated rotor speed value. The disturbance injected by process of speed estimation in the usual encoderless predictive methods is removed from the system. The experimental results show that the proposed algorithm can work well and achieve good performance at a very wide range of speeds. [ABSTRACT FROM PUBLISHER]

In this paper, a simple model predictive current control strategy is proposed for four-leg inverters to deliver power to the (un)balanced/(non)linear loads. Unlike classical control schemes, the proposed method does not require current PI controllers or a complex modulation stage. The discrete-time model of the inverter, filter, and load are used to predict the future behavior of the load currents for each of the 16 possible switching states. The control method chooses a switching state that minimizes the error between the output currents and their references. The performance of the proposed method has been investigated for perturbations in the filter and load parameters. The neutral-leg switching frequency reduction algorithm is also proposed to improve the efficiency of the converter. The feasibility of the proposed method is verified in this first part, through simulation results. [ABSTRACT FROM PUBLISHER]