FCS-MPC-Based Current Control of a Five-Phase Induction Motor and its Comparison with PI-PWM Control

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.

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.

Finite control set model predictive control (FCS-MPC) of three-level neutral point clamped inverter-fed induction motor has received wide concern recently, thanks to its low switching frequency and fast dynamics. However, the performance largely depends on the accurate measurement and parameters which determine the feedback and control loops, respectively. Besides, FCS-MPC demands a large number of resources to perform the exhaustive search, which indicates that the computation burden increases exponentially with the increasing switching states. Aiming at improving the robustness under the condition of unavoidable measuring noises and parameter variation as well as reducing the computational burden, a passivity-based model predictive control (PB-MPC) scheme is presented in this article. For an ideal system, the PB-MPC has a similar character as that of the FCS-MPC. In real applications, where noises and disturbances impact the system more or less, PB-MPC outperforms FCS-MPC due to the power shaping and damping injection inherited from passivity-based control which ensures the asymptotic stability. A Lyapunov function is designed to prove the stability of the proposed scheme. The stability and efficiency are verified by both simulation and experimental results.

This paper evaluates the performance of strategies based on finite-control-set model predictive control (FCS-MPC) aimed at reducing or fixing the converter switching frequency or decreasing the spread of the harmonic spectrum in multilevel hybrid cascade converters (HCCs). These properties are desirable for medium- to high-voltage applications, where minimizing switching losses is crucial, as well as for applications employing passive filters, where resonance modes can be excited. The strategies evaluated are input restriction, notch filtering, period control, and PWM restriction. Key aspects considered in this work are (i) the evaluation of the steady-state and transient performance of FCS-MPC strategies proposed for two-level converters in a multilevel topology, and (ii) the evaluation of the computational cost associated with the implementation of these strategies on a multilevel converter with a high number of available inputs. As a typical application, the study is carried out employing a five-level HCC experimental prototype driving an induction motor through indirect vector control. To perform a fair comparison between the strategies, a control platform based on a cost-effective Zynq system on chip is proposed, which allows for achieving the hard timing constraints imposed by FCS-MPC strategies. The results show that the PWM restriction strategy achieves the best steady-state performance among the evaluated strategies, with an error 400 times smaller than that of the second-best strategy (input restriction), with an average switching frequency of 962.5 Hz, which differs from the desired average frequency by 3%, and a maximum difference in power distribution between modules of 0.8%. In addition, the system-on-chip hardware achieves a competitive execution time of 46 μs when the ARM Cortex solution is implemented and 20 μs when the ARM Cortex–FPGA solution is used instead, employing the 512 inputs available in the FCS-MPC algorithm. The studies, performed in steady-state and transient regimes, confirm (i) the feasibility of the evaluated algorithms in an HCC topology and (ii) the feasibility of the control platform for implementing high-computational-burden algorithms with a low sampling time.

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

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.

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.

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.

The conventional model predictive torque control (MPTC) for induction motor drives evaluates the electromagnetic torque and stator flux linkage in the cost function. However, in the speed outer loop design, the torque reference in the cost function generally utilizes a classic proportional-integral (PI) controller, which is not only dependent on motor parameters but also sensitive to the change of the load torque. In this article, an adaptive sliding-mode-control-based MPTC (ASMC-MPTC) method is proposed to improve the robustness of the finite control set model predictive control (FCS-MPC). First, the influence of the mismatched parameters for FCS-MPTC is introduced and the shortcoming of the PI-based MPTC (PI-MPTC) is analyzed. Then, a sliding-mode-control-based MPTC (SMC-MPTC) is studied, in which the exponential reaching law is introduced to control the trajectories of the state variables. To further improve the control performance of SMC-MPTC, the exponential reaching law is optimized to adjust the switching gain adaptively, thus the ASMC-MPTC is proposed. The PI-MPTC, SMC-MPTC, and ASMC-MPTC are compared theoretically and experimentally, and the results show that the proposed ASMC-MPTC has the best disturbance rejection ability and robustness against motor parameters variation and load torque change.

In this article, sensorless finite control set model predictive speed control of induction motor using adaptive full-order observer is proposed. Firstly, finite control set model predictive speed control is introduced. It is composed of direct speed control and model predictive control, which gets over the limitations of cascaded structure and obtains high dynamics response. Due to lack of speed loop, stator flux linkage, rotor speed and stator current are taken as the control targets in the cost function. Then, adaptive full-order observer is used to estimate rotor speed and stator flux linkage to improve the reliability and keep the system cost down. Finally, the simulation results indicate that the proposed method has well dynamic performance in the wide speed range.

This paper investigates the study and practical implementation of Finite Control Set Model Predictive Control (FCS-MPC) for a five-phase induction motor powered by a two-level, five-phase voltage source inverter. Multiphase systems present notable benefits over conventional three-phase configurations, including improved fault tolerance, enhanced torque production, and minimized harmonic distortion, making them well-suited for demanding applications. FCS-MPC operates by directly predicting and selecting the optimal control action at each sampling instant, eliminating the necessity of a modulation stage and offering fast dynamic response. The proposed control approach is designed and validated through experimental testing, assessing key performance indicators such as speed tracking, torque ripple, and current distortion under both transient and steady-state conditions. The experimental results demonstrate the effectiveness of FCS-MPC in ensuring precise and efficient control across various operating scenarios, making it a viable alternative for multiphase motor drive applications.

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.

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 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.