Direct model predictive control (MPC) with reference tracking, also referred to as finite control set MPC (FCS-MPC), has gained significant attention in recent years, mainly from the academic community. Thanks to its applicability to a wide range of power electronic systems, it is considered a promising control method for such systems. However, to simplify the design, researchers frequently make choices that—often unknowingly—reduce the system performance. In this article, we discuss and analyze the factors that affect the closed-loop performance of FCS-MPC. Based on these findings, design guidelines are provided that help to maximize the system performance. To highlight the performance benefits, two case studies will be considered: the first one consists of a two-level converter and an induction machine, whereas the second one adds an $LC$ filter between the converter and the machine.

Guidelines for the Design of Finite Control Set Model Predictive Controllers

This paper proposes the application of a finite control set model predictive control (FCS-MPC) strategy in standalone ac microgrids (MGs). AC MGs are usually built from two or more voltage source converters (VSCs) which have the capability of regulating the voltage at the point of common coupling, while sharing the load power at the same time. Those functionalities are conventionally achieved by hierarchical linear control loops. However, they present severe limitations in terms of slow transient response and high sensitivity to parameter variations. This paper aims to mitigate these problems by first introducing an improvement of the FCS-MPC strategy for a single VSC that is based on explicit tracking of derivative of the voltage reference trajectory. Using only a single step prediction horizon, the proposed strategy exhibits very low computational expense, but provides steady-state performance comparable to carrier-based sinusoidal PWM, while its transient response and robustness to parameter variation is far superior to hierarchical linear control. These benefits are exploited in a general ac MG setting where a methodology for paralleling multiple FCS-MPC regulated VSCs is described. Such an MG is characterized by rapid transient response, inherent stability in all operating conditions, and fully decentralized operation of individual VSCs. These findings have been validated through comprehensive simulation and experimental verification.

Model Predictive Control of Power Converters for Robust and Fast Operation of AC Microgrids

Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices, and parameters that affect the system performance, closed-loop stability, and controller robustness are discussed. Moreover, solvers, and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design, and computational complexity, along with their performance, and applicability depending on the power electronic system at hand.

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Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

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.

Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

To reduce the steady-state torque ripples for permanent-magnet synchronous motor drives with predictive torque control (PTC), this paper proposes a modified PTC algorithm based on the extended control set (ECS-PTC). In the proposed algorithm, more candidate voltage vectors are extended to the control set, which forms the ECS, and then, the torque control precision could be improved and the torque ripples will be reduced. More attractive thing is that a new voltage vector synthesis method is deigned in ECS-PTC, and the corresponding predictive model and cascaded predictive algorithm are set up on the basis of this method. With the help of them, the optimal vector can be selected from ECS in a shorter control period of time, and its amplitude is also optimized at the same time to achieve high-precision control effects for torque and flux. In addition, the three-phase duty ratios of the output vector can be determined in ECS-PTC directly, without the help of space vector pulse width modulation. The experimental results show that ECS-PTC has excellent dynamic torque performance, and it also has more stable torque control performance and better execution efficiency at the same time.

Torque Ripple Minimization of Predictive Torque Control for PMSM With Extended Control Set

Finite control set-model predictive control (FCS-MPC) is emerging as an attractive alternative for power converters control. In comparison with the classical linear controllers, FCS-MPC needs a shorter control loop cycle time to achieve the same performance. But, the conventional FCS-MPC involves a large amount of calculation. The calculation efforts increase with the number of switching states of the power converter as well as the control objectives, which is a challenge for its application. This paper proposes an effective method to simplify the FCS-MPC and to reduce its calculation efforts for its application in matrix converter-fed permanent magnet synchronous motors. The experimental results which verify the good performance of the proposed method are presented.

Simplified Finite Control Set-Model Predictive Control for Matrix Converter-Fed PMSM Drives

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.

Torque Ripple Reduction of Predictive Torque Control for PMSM Drives With Parameter Mismatch

Finite control set-model predictive control (FCS-MPC) is an interesting alternative for the control of drive systems. However, FCS-MPC requires a large amount of calculation, because it uses all feasible voltage vectors of a power converter for prediction and evaluation. This is an obstacle for its application when the number of voltage vectors of power converter is increased. The computational burden is more challenging when the control objectives are increased. At the same time, matrix converter is an attractive alternative to conventional back-to-back converters with dc link. However, implementation of FCS-MPC for a matrix converter is computationally expensive due to the 27 feasible voltage vectors of the matrix converter. In this paper, a Lookup table is used to reduce the candidate voltage vectors that make the FCS-MPC computationally efficient for matrix converter-fed permanent-magnet synchronous motors. This approach is implemented experimentally and is compared with the conventional approach to evaluate its performance.

A Computationally Efficient Lookup Table Based FCS-MPC for PMSM Drives Fed by Matrix Converters

Predictive control has emerged as an attractive alternative for the control of matrix converters. The predictive control is a flexible method to control different variables through the minimization of a cost function. In drive system, the predictive control can be divided to predictive current control (PCC) and predictive torque control (PTC). In PCC, the stator current is evaluated in cost function, but in PTC the torque and stator flux are evaluated in the cost function. On the other hand, the ability of matrix converter in control of input source current can be easily included in both PCC and PTC. This paper introduces the methodology and the results of a comprehensive comparison of PCC and PTC techniques for a permanent-magnet synchronous motor (PMSM) fed by a matrix converter. The comparison involves the investigation of the torque and stator flux ripples, the total harmonic distortion of input and output currents, and the input filter resonance as figures of merit. Different performance indicators that present an instruction for the selection of predictive control methods for PMSM drive applications by matrix converters for engineering practices are provided with this comparative evaluation.

Mohsen Siami

Papers

Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

Simplified Finite Control Set-Model Predictive Control for Matrix Converter-Fed PMSM Drives

Torque Ripple Reduction of Predictive Torque Control for PMSM Drives With Parameter Mismatch

A Computationally Efficient Lookup Table Based FCS-MPC for PMSM Drives Fed by Matrix Converters

An Experimental Evaluation of Predictive Current Control and Predictive Torque Control for a PMSM Fed by a Matrix Converter