Symmetrical Three-Vector-Based Model Predictive Control With Deadbeat Solution for IPMSM in Rotating Reference Frame

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.

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.

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.

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.

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.

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) 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.

Improved model predictive current control (MPCC) strategies in 3-D space are proposed in this article for an open-winding permanent-magnet synchronous motor (OW-PMSM) system with a common dc bus. In order to simply determine the optimal voltage vector (VV), a cost function related to VVs is established to replace that related to stator currents in conventional MPCC and the weighting factors are obtained by theoretical derivation. In order to reduce the calculation time of the optimal VV, a 3-D space system is established and the candidate VVs and the reference VV can be placed in the 3-D space. Then by means of the space geometric theory, the optimal VV can be selected faster and only four calculations are required in each sampling period. For better tracking of the reference VV, a VV-tracking (VVT) MPCC strategy is proposed and three VVs are selected according to the volt-second balance principle. Experimental results verify the effectiveness of the proposed strategies.

This paper proposes a finite control set model predictive control (FCS-MPC) to guarantee the stability and robustness for surface-mounted permanent magnet synchronous motor (SPMSM) drives. Continuous-input-based control laws are first developed from a control-Lyapunov function in order to both stabilize the closed-loop system via feedback control laws and ensure the robustness via online adaptive laws. Because the asymptotic stability of the proposed control method is guaranteed by at least one discrete switching-state, the continuous-input-based control laws are converted into relevant constraints of the FCS-MPC optimization problem. To validate the advantages of the proposed FCS-MPC, comparative studies with the conventional FCS-MPC and space vector modulation based adaptive control are conducted on a prototype SPMSM testbed with a TI TMS320F28335 DSP. The effectiveness of the proposed FCS-MPC is verified by the comparative schemes with/without the additional constraints. Superiority of the proposed schemes such as zero steady-state error, fast speed-tracking capability, well-regulated stator currents, and low average switching frequency are experimentally validated under the step-changes of load torque and speed reference.

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.

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.

The traditional finite-control-set model predictive control (FCS-MPC) method for a cascaded H-bridge (CHB) rectifier has two main issues: heavy computational burden and low steady-state current performance. In this paper, a novel FCS-MPC method has been proposed for a single-phase CHB rectifier. The proposed method solves the optimization problem of FCS-MPC for one cell by one cell, like a “pipeline.” In the proposed method, the sampling period is divided into equal intervals by the number of cells. At the beginning of the first interval, the first cell selects its switching state to be applied. Then, the following cell selects its switching state to be applied at the beginning of next interval. Finally, the selected switching state of last cell will be applied at the beginning of the last interval. A cost function presenting the control objectives of common source current error and itself dc-link voltage error is evaluated for each cell. A single-phase three-cell CHB rectifier controlled by a DSpace DS1104 is tested and the experimental results show that a significant reduction in computational time, an improved steady-state current performance, and a comparable dynamic response are achieved in the proposed method in comparison with the traditional FCS-MPC method.

This paper deals with a particular problem in the conventional finite set model predictive control (FS-MPC) method in VSI drives. When FS-MPC is experimentally implemented, the controller chooses any one of the eight available voltage vectors. Therefore, if the controller selects any of the far states, simultaneous switching events among the inverter legs occur. This causes line-line voltage polarity reversal (LLVPR) with the voltage change twice the value of the DC-link voltage. The fast LLVPR combined with the reflected wave by the motor cable can damage the insulation of the stator winding. In this letter, the problem of LLVPR in electric drives with FS-MPC is described, and the solution to this issue is proposed. Experimental results to validate the proposed method are shown.

One of the main distinctive features of multiphase machines is the appearance of new degrees of freedom ( - voltages/currents) that do not exist in their three-phase counterparts. As a direct consequence, control approaches that apply a single switching state during the sampling period cannot achieve zero average - voltage production. In direct torque control (DTC) this implies that - currents are not regulated, whereas in finite-control-set model predictive control (FCS-MPC) an enhanced - current regulation is feasible only at the expense of disturbing the flux/torque production. Aiming to avoid these shortcomings, this work makes use of the concept of synthetic/virtual voltage vectors (VVs) to nullify/limit the - voltage production in order to improve the current regulation in the secondary planes. Two strategies using two and four virtual voltage vectors (2-VV and 4-VV, respectively) are proposed and compared with the standard case that applies a single switching state. Since standard MPC has the capability to indirectly regulate - currents, the improvements with the inclusion of VVs are expected to be more significant in DTC strategies. Experimental results validate the proposed VVs and confirm the expectations through a detailed performance comparison of standard, 2-VV and 4-VV approaches for DTC and MPC strategies.