Direct Torque Control of PMSM with Modified Finite Set Model Predictive Control

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.

Three‐phase induction motor (IM) drives play an important role in the variable speed applications from fractional horsepower to multi Megawatt power; it controls and evaluates them to be an interesting subject. This study presents a novel model‐predictive direct torque control (MPDTC) for IM drives. It combines the direct torque control (DTC) and finite control set model‐predictive control (FCS‐MPC) methods, and uses the predictive look‐up tables to enhance the overall performance of IM as the good steady‐state performance, lower switching frequency, and to achieve relatively low computational complexity. The simulations and experiments are performed to verify the effectiveness of the proposed control strategy. As the main conclusion of this work, it overcomes the disadvantages of the DTC method such as the high torque and stator flux ripples and those of the FCS‐MPC method such as the large amount of calculation and high average switching frequency. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper presents an improved model predictive direct torque control (MPDTC) with duty cycle optimization for permanent magnet synchronous motor (PMSM). The conventional direct torque control (DTC) or MPDTC achieves good dynamic and steady state performance by selecting an appropriate voltage vector. However, there is only one voltage vector in one control period, hence the ideal performance of torque can't be achieved due to the limited number of voltage vectors. In this paper, the concept of duty cycle is applied to MPDTC for PMSM. This method allocates a fraction of one control period for a nonzero voltage vector and the rest of the time for a zero voltage vector. A nonzero voltage vector is selected by minimizing a cost function and its duration time is achieved by the errors minimization of torque and stator flux. Simulation and experimental results show that, the proposed MPDTC with duty cycle control achieves adequate steady state torque performance without affecting the dynamic response, compared with the traditional DTC and conventional MPDTC.

This Paper evaluates the integration of fuel cell power system with the grid using finite control set model predictive controller. A mathematical model of the phosphoric acid fuel cell power system is designed. The grid and the fuel cell are modeled in PSCAD simulation environment. Direct power control technique for model predictive controller is used to control the converter. The steady state results are shown in the paper, Also, a single phase fault and three phase fault is applied at the grid side to study the effect of integrating the fuel cell with the grid. The simulation results show model predictive controller can effectively integrate fuel cell with the grid.

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.

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

This paper proposes a torque control method for surface mounted permanent magnetic synchronous motor (PMSM) driven by a 2-level voltage source driven inverter, which has fast torque response and small torque ripple. The proposed torque control method follows the finite control set model predictive control (FCS-MPC) strategy. A reference state is derived at each time step for the given time varying torque reference and the cost index is defined so that the tracking error for this reference state should be penalized. The choice of an optimal output voltage vector is made first from the 6 possible active voltage vectors of the 2-level voltage source inverter. Then a modulation factor for the chosen optimal voltage vector is obtained so that the torque ripple can be reduced further. It is shown that the proposed FCS-MPC control method yields fast torque tracking response and small torque ripple through simulation and experiments.

<span>This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>

Finite control set model predictive control (FCS-MPC), especially the reference-voltage-vector-based model predictive control (MPC) (RVV-MPC), considers a limited set of input vectors. However, the control performance is not satisfactory enough due to a severe lack of voltage input variants. In particular, additional restraints, such as limiting the currents within setting boundaries, could not be supported with RVV-MPC. In this article, to truly comply with the boundary settings, a reference variant FCS-MPC approach is introduced. In the proposed strategy, reference currents are considered under the maximum torque per current (MTPC) control. As boundary settings could not be supported in RVV-MPC, it is better to search for the optimal current reference that is feasible within the imposed boundaries. Therefore, rather than fixing the current reference, a well-considered neighborhood of the MTPC reference is introduced, and these variants are processed, resulting in a set of optimal candidate inputs. As multiple, but still finite, optimal solutions become possible, additional efforts can be made within the objective function to restrain the current and reduce the torque ripple. The size of the current reference space can be automatically adapted to generate current references that correspond to truly bounded current vectors. A trade-off is then made between control performance and meeting the boundary settings.

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 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.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

In this paper, a modified control strategy of Finite-Control-Set Model Predictive Control (FCS-MPC) for the Interior Permanent Magnet Synchronous Motor (IPMSM) is proposed to solve the problem that the actual stator current cannot quickly track the given Maximum Torque Per Ampere (MTPA) current due to the integration saturation of the current regulator caused by sudden changes in load torque. The FCS-MPC controller is used to replace the PI current regulator, to obtain a more accurate voltage vector and achieve real-time dynamic tracking of the IPMSM MTPA target trajectory. The cost function of FCS-MPC control strategy is designed multi-objective combination to meet such requirements: torque tracking, MTPA current tracking and limitation of the maximum current. The simulation and experimental results show that the proposed control strategy can track the MTPA given current and torque in real time during load torque sudden changes.

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 paper presents the use of the fuzzy logic duty cycle concept in the finite-control-set model predictive direct torque control (FCS-MPDTC) strategy to mitigate the torque ripples of a surface-mounted permanent magnet synchronous motor (PMSM) drive. In fact, MPDTC is a simple and powerful tool to overcome the main drawbacks associated with the commonly used control strategies like direct torque control (DTC) and field-oriented control (FOC). However, the conventional-MPDTC selects only one voltage vector (VV) per sampling period and this procedure leads to increase relatively the torque ripple. In order to further improve the torque control performance, an adaptive fuzzy logic-based duty cycle modulation is utilised and two VV's instead of single VV are applied during the whole control cycle. The performances of the proposed control scheme are simulated and compared to those obtained with conventional-MPDTC. The simulation results show that the proposed AFLM-MPDTC can effectively mitigate the torque ripples, even under severe operating conditions.