전기자동차용 표면 부착형 영구자석 동기 전동기의 토크제어를 위한 유한 제어 요소 모델 예측제어(FCS-MPC) 기법

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.

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%.

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.

Due to the large number of candidate voltage vectors, there is heavy calculation burden of finite control set-model predictive control (FCS-MPC) in multiphase open-winding (OW) motor drive systems. Though low-complexity FCS-MPC schemes exist, they are sensitive to motor parameters. In this article, a robust FCS-MPC with high computational efficiency is proposed for the nine-phase OW-PMSM. First, based on the virtual voltage vector scheme synthesized by three active vectors, harmonic voltage components are eliminated. Then, the deadbeat current control is introduced to calculate the reference voltage vector (RVV), and the optimal voltage vector is determined by the location of the RVV, which successfully reduces the number of iterations in selecting the voltage vectors and improves the computational efficiency. In addition, in order to mitigate the sensitive of the proposed scheme to motor parameters, a parameter extraction method based on a sliding mode observer is designed to enhance the robustness of the FCS-MPC. Moreover, the corrected parameters can also be used for other calculations to achieve satisfactory steady-state performance, such as duty cycle optimization. Finally, experimental results verify the feasibility and superiority of the proposed method.

Recently, the finite control set model predictive control (FCS-MPC) strategy has been widely used in power electronics and motor drives. However, the implementation of the traditional FCS-MPC strategy needs to traverse all possible switch combinations, which greatly increases the computational burden of the system. In order to address this problem, this paper proposes a simplified FCS-MPC strategy based on historical data to enhance the performance of the system. In the proposed strategy, the historical data of the optimal vector in the last fundamental frequency period is stored and it is then utilized as the reference when selecting the optimal vector in the current fundamental frequency period. Also, the optimal vector is selected from the adjacent vectors and the zero vector. A simulation model of a three-phase LCL-type grid-connected inverter is established to verify the performance of the proposed control strategy.

In this paper, a simplified finite‐control‐set model predictive control (FCS‐MPC) method with a deadbeat solution is proposed for eight‐switch, three‐phase, neutral‐point‐clamped converters (ESTP‐NPCs). Specifically, in order to improve the computational efficiency, a novel sector distribution scheme is presented by dividing the whole voltage vector space into eight sectors. In the each sector, only three voltage vectors are determined as new candidate finite control sets (FCSs), and then the optimal voltage vector that minimizes the predesigned cost function is selected from the new candidate FCS. Meanwhile, in order to avoid the selection of the weighting factor, a multiobjective optimization strategy based on an average ranking is introduced into the proposed strategy. In addition, the DC‐link capacitor voltage oscillations and deviations are suppressed effectively, and the time delay of control command is compensated. In view of these characteristics, the proposed control strategy (i) has lower computational burden, (ii) has good dynamic performance, (iii) does not need the tuning of weighting factor, and (iv) does not need the modulation method. Finally, compared to the conventional FCS‐MPC, the proposed method is more efficient as demonstrated by simulation results. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Conventional model predictive current control (MPCC) applies only one vector during one control period, which produces large torque and flux ripples and high current harmonics in permanent magnet synchronous motor (PMSM) drives. Recently MPCC with duty cycle control has been proposed to improve the steady state performance by applying one non-zero vector and one null vector during one control period. However, the prior method requires lots of calculations and predictions to find the optimal voltage vectors and calculate their respective duration. Different from prior enumeration-based MPCC, this paper proposes an efficient two-vector MPCC by applying two arbitrary voltage vectors during one control period. The reference voltage vector is firstly calculated based on the principle of deadbeat current control. Two optimal vectors and their duration are then obtained in a very efficient way, which does not require the calculation of current slopes in prior MPCC methods. The proposed method is compared to the state-of-the-art predictive control methods, including conventional MPCC, MPCC with duty cycle control, deadbeat control with space vector modulation (SVM) and modulated model predictive control (M2PC). Both simulation and experimental results prove that the proposed method achieves better steady state performance than conventional MPCC with or without duty cycle and the dynamic response is not degraded. Under the condition of insufficient dc bus voltage, the proposed method outperforms deadbeat control and M2PC by presenting even higher speed range and less torque ripples.

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 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 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.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

The increasing demand for electrified transportation requires high-power, efficient, and reliable traction drive systems. Dual three-phase permanent magnet machines (DTP-PMSMs) with high saliency ratios offer key advantages for these applications but introduce challenges in control accuracy and current harmonic suppression. This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with an enhanced sector selection method specifically tailored for high-saliency DTP-PMSM drives. The proposed approach improves actuation accuracy in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> subspace by reliably identifying the optimal virtual vectors, while a quasi-proportional resonant (QPR) controller effectively suppresses current harmonics in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x-y</i> subspace. Simulation and experimental results demonstrate that the proposed method achieves a fast dynamic response, reduced current ripple, and strong current harmonic suppression, outperforming existing virtual vector-based FCS-MPC and field-oriented proportional-integral (PI) control methods. The results confirm the robustness and efficacy of the proposed controller across a wide range of operating conditions, making it a promising solution for high-performance electric drives.

An improved finite control-set model predictive control for nested neutral point-clamped converters under both balanced and unbalanced grid conditions

Finite set model predictive control (FS-MPC) has become a promising control technology in power converter, because of the advantages of good dynamic response and accurate current tracking capability. In real-time control process, the conventional FS-MPC strategy requires more time for prediction and optimization. As a result, there will be a certain delay between sampling and output. In order to reduce the amount of calculation and overcome the adverse effect of the delay on the system performance, this paper presents a simplified FS-MPC method. Firstly, adjacent levels method is used to reduce the amount of the calculation, and then two-step FS-MPC is adopted to compensate the calculation delay. The control strategy is validated by the simulation and experimental results of a grid-connected cascaded H-bridge converter.

Model predictive control has been widely applied to AC electric drives over the last decade. Despite the proposed solutions, researchers are still seeking to find more effective solutions for weighting factor design, parameter dependency, current/torque harmonics, variable switching frequency, and computational complexity. This paper presents a comprehensive review of the weighting factor design techniques for finite control set model predictive control strategies for AC electric drives. First, the paper introduces the conventional model predictive control techniques for electric drives over permanent magnet synchronous motors. Second, weighting factor design methods are discussed under two main headings: weighting factor selection and weighting factor elimination methods. Third, the ongoing challenges and future trends are addressed by considering the current literature. Based on this review, it is obvious that each weighting factor design method still has problems that await more effective solutions. Finally, this paper reviews various weighting factor design methods for AC electric drives, reveals the advantages and disadvantages of existing methods in terms of control performance, flexibility, design complexity, and computational complexity, and highlights future trends.