Model Predictive Torque Control Research Articles

Dual three-phase permanent magnet synchronous motors model predictive torque control based on zero common-mode voltage

This article proposes a multi-vector model predictive torque control strategy based on zero common-mode voltage to suppress common-mode voltage and harmonic currents in a dual three-phase permanent magnet synchronous motor. Firstly, 20 zero common-mode voltage vectors are selected from the traditional 64 basic voltage vectors. By synthesizing these 20 vectors into 6 virtual voltage vectors (VVV), control over the harmonic plane is achieved, reducing harmonic currents and eliminating common-mode voltage. Then, the control error of the flux is reduced by increasing the number of vectors to achieve the purpose of reducing the flux ripple. Finally, through duty cycle modulation, multi-vector model predictive torque control extends the modulation range to the entire sector plane. Simulation results confirm that this strategy can effectively suppress harmonic currents and common mode voltage.

An Improved Model-Predictive Torque Control of Switched Reluctance Motor Based on Sector Adaptive Allocation Technology

An Improved Model-Predictive Torque Control of Switched Reluctance Motor Based on Sector Adaptive Allocation Technology

Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor

Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor

Three-Vector-based Model Predictive Torque Control for Dual Three-Phase PMSM With Torque and Flux Ripples Reduction

Three-Vector-based Model Predictive Torque Control for Dual Three-Phase PMSM With Torque and Flux Ripples Reduction

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

Model Predictive Torque Control for Dual Three-Phase PMSM based on Novel Virtual Voltage Vector

Model Predictive Torque Control for Dual Three-Phase PMSM based on Novel Virtual Voltage Vector

Predictive Torque Control of the Vehicle’s Permanent-Magnet Synchronous-Motor Model Based on Multi-Objective Sorting

The permanent-magnet synchronous motor (PMSM), with the advantages of low energy consumption and stable operation, is considered a green power source to replace gasoline engines. Motor control is the core problem of the electric-drive system, so it is important to study the high-performance motor control algorithm. The traditional PMSM control strategy has problems such as torque pulsation, large overshoot, and parameters which are not easy to adjust. This work proposes a new model-predictive torque control (MPTC) based on multi-objective ranking for these issues. The Romberg observer was utilized to accurately estimate motor flux and torque across a wide range of speeds and ensure optimal performance of the MPTC. The optional voltage vectors were classified using graph theory. The model’s cost function was optimized and the control delay caused by hardware processing was compensated by a modified Euler method. A multi-objective ranking method was used to avoid the offline selection of MPTC weight coefficients. Additionally, one ranking method was used to reduce the complexity of the algorithm for multiple objectives. Based on the simulation results, the newly proposed MPTC method, when compared with traditional approaches, reduced the total harmonic distortion from 2.78% to 2.26%. Torque ripple decreased by approximately 58.4%, and the switching frequency was reduced by 3.05%, lowering the inverter’s switching losses. Therefore, the newly proposed MPTC had faster torque response, reduced computation time, and less torque pulsation, which further improved the dynamic performance of the permanent-magnet synchronous motor.

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function

Three-Stage Duty Cycle-Based Deadbeat Predictive Torque Control for Three-Phase SPMSMs With CMV Reduction

Model Predictive Torque Control (MPTC) is highly regarded for its simple structure, rapid dynamic response, and optimized control performance when applied to actual motor driving systems. However, conventional MPTC techniques often lead to undesirable effects in three-phase inverter-based surface-mounted permanent magnet synchronous motors (SPMSMs), such as high electromagnetic torque, stator flux ripples, and significant current harmonic distortion. Notably, the generalized MPTC approach generates a substantial common mode voltage (CMV) that can cause damage to the motor bearing and winding insulation in SPMSMs. To address these issues, this paper proposes a three-stage duty cycle-based deadbeat predictive torque control (DCDPTC) technique for SPMSMs. The proposed technique leverages the volt-second equilibrium theorem by aligning the duty cycles with the predicted electromagnetic torque, stator flux, and their respective target values while accounting for extreme cases, accordingly electromagnetic torque and stator flux ripples as well as the total harmonic distortion (THD) of the stator current are minimized. In order to improve the system robustness, the duty cycle is limited to avoid the overmodulation. Furthermore, optimized switching sequences are meticulously arranged to ensure the CMV is lowered and the minimum number of switching transitions is maintained while utilizing a fixed switching frequency. The proposed technique is simplified. Its resultant performance is validated by experimental results.

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.

Performance Enhancement of a Variable Speed Permanent Magnet Synchronous Generator Used for Renewable Energy Application

The paper aims to develop an improved control system to enhance the dynamics of a permanent magnet synchronous generator (PMSG) operating at varying speeds. The generator dynamics are evaluated based on lowing current, power, and torque ripples to validate the effectiveness of the proposed control system. The adopted controllers include the model predictive power control (MPPC), model predictive torque control (MPTC), and the designed predictive voltage control (PVC). MPPC seeks to regulate the active and reactive power, while MPTC regulates the torque and flux. MPPC and MPTC have several drawbacks, like high ripple, high load commutation, and using a weighting factor in their cost functions. The methodology of designed predictive voltage comes to eliminate these drawbacks by managing the direct voltage by utilizing the deadbeat and finite control set FCS principle, which uses a simple cost function without needing any weighting factor for equilibrium error issues. The results demonstrate several advantages of the proposed PVC technique, including faster dynamic response, simplified control structure, reduced ripples, lower current harmonics, and decreased computational requirements when compared to the MPPC and MPTC methods. Additionally, the study considers the integration of blade pitch angle and maximum power point tracking (MPPT) controls, which limit wind energy utilization when the generator speed exceeds its rated speed and maximize wind energy extraction during wind scarcity. In summary, the proposed PVC enhanced control system exhibits superior performance in terms of dynamic response, control simplicity, current quality, and computational efficiency when compared to alternative methods.

Finite Control Set Model Predictive Torque Control Considering Dynamic and Steady‐State Control Performance for SMPMSM Drives

In order to improve the dynamic and steady‐state performance of the finite control set model predictive torque controlled surface‐mounted permanent magnet synchronous motor drive system, a novel finite control set model predictive torque control (FCS‐MPTC) with different dynamic and steady‐state control schemes is proposed in this paper. When the SMPMSM drive system operates at transient state, the optimal basic voltage vector of inverter is the vector that minimizes torque tracking error, the proposed FCS‐MPTC achieves fast dynamic response based on the optimal torque control. When the system operates at steady state, the relative importance of torque to the stator flux amplitude is calculated based on the quantitative analysis of minimum staggered errors between motor torque and stator flux amplitude control objectives, then the priority weighting factors based on the relative importance are determined online adaptively, the optimal control of motor torque and stator flux amplitude is achieved simultaneously. Finally, the comparison of the experimental results of proposed FCS‐MPTC with different control methods is implemented and research conclusion is shown. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Enhancement of Weighting Coefficient Selection using Grey Relational Analysis for Model Predictive Torque Control of PMSM Drive: Analysis and Experiments

Permanent magnet synchronous motor (PMSM) with model predictive torque control (MPTC) is popular for its simplified control structure and adaptable in incorporating control parameters into the control algorithm. However, in control technique the primary concern for objective function (OF) depends on the selection of appropriate weighting coefficient (WC). Basically, for weighting coefficient selection, empirical methods are used but it takes additional time and heuristic process. In this paper, Grey Relational Analysis (GRA) technique is introduced in optimization of objective function for selection of appropriate weighting coefficient. In this methodology, stator flux and torque having individual OF are modified from single-OF. This ensures that in each sampling period, selection of grey relational optimal control action is dependent on the preference given to the control parameters in OF. For each sampling, a Grey Relational Grade (GRG) is employed to determine the appropriate control action. The models for two-level inverter fed PMSM are developed in MATLAB/Simulink to test the various operations of PMSM drive and the results are validated on the experimental test bench using dSPACE-1104 R&D controller. In order to highlight the effectiveness of the proposed technique, the results are compared with DTFC and MPTC approach.

Model predictive torque control for permanent magnet synchronous motor with hybrid mode duty cycle optimization

AbstractDue to the application of only one voltage vector in a control period, conventional model predictive torque control (MPTC) suffers from relatively large steady‐state ripples. The‐state‐of‐the‐art double‐vector‐based MPTC (DVMPTC) was proposed to improve steady‐state performance. However, its duty cycle optimization method significantly affects the control performances. Thus, MPTC with hybrid mode duty cycle optimization is proposed, which realizes better performance than the universal DVMPTC, and obtains the appropriate average switching frequency, especially at high‐speed domain. The proposed method utilises the universal DVMPTC method, while it equips duty cycle optimization with discrete space vector modulation (DSVM) scheme as the additional constraint for further reducing the reference voltage tracking error that exists in DVMPTC. What's more, rotation module and pre‐selection strategies are involved in simplifying the design of vector selection methods. Comparative simulations and experiments verify the effectiveness of the proposed method.

Incorporating Harmonic-Analysis-Based Loss Minimization Into MPTC for Efficiency Improvement of FCFMPM Motor

According to air-gap field-modulation theory, multiple field harmonics are responsible for high torque density of field modulation (FM) motors. However, abundant field harmonics will undoubtedly increase motor losses, especially iron loss and PM eddy-current loss. Therefore, a harmonic-analysis-based loss minimization (HLM) method incorporated into model predictive torque control (MPTC) for efficiency improvement of FM motors is proposed in this article. First, by analyzing the loss generation mechanism, a harmonic-based online loss calculation model is built to serve for loss minimization control. Then, the reference flux for MPTC can be derived from the proposed HLM method, which avoids complexity of equivalent resistance tuning and improves accuracy of loss calculation compared to the conventional model-based loss minimization method which lacks attention on the harmonic-based loss analysis. Consequently, the HLM-MPTC strategy is finally determined and then applied in a flux-concentrating field-modulation permanent-magnet prototype to verify its validity. The experimental results evidence that the proposed HLM-MPTC can effectively achieve loss reduction and efficiency improvement.

Modulated Model Predictive Torque Control for Fault-Tolerant Inverter-Fed Induction Motor Drives With Single DC-Link Voltage Sensor

Four-switch three-phase inverter (FSTPI) is widely used as a cost-effective fault-tolerant topology of six-switch three-phase inverter with an open-phase fault. To solve the inherent dc-link capacitor voltage offset (CVO) issue of FSTPI, two dc-link capacitor voltage sensors are required in conventional model predictive torque control (MPTC) strategies. To improve the reliability and reduce the cost of induction motor (IM) drives, a modulated MPTC (M-MPTC) for FSTPI-fed IM with single dc-link voltage sensor is proposed in this article. Therein, the relationship of the CVO and the load current is established to obtain the fundamental component of CVO. The two capacitor voltages are reconstructed by single dc-link voltage sensor and the fundamental component of CVO. To suppress the CVO, a cost function that only contains the output voltage vector is designed. The inner relationship between the proposed M-MPTC with single dc-link voltage sensor and the conventional M-MPTC with two dc-link voltage sensors is derived. To retain the load current within the allowed range, a current constraint scheme is proposed and integrated into the M-MPTC. Various simulation and experimental studies are carried out to verify the effectiveness of the proposed strategy.

An Improved Model Predictive Torque Control for PMSM Drives Based on Discrete Space Vector Modulation

In this paper, an improved model predictive torque control (MPTC) method based on discrete space vector modulation (DSVM) is proposed for permanent magnet synchronous motor (PMSM) drives. Aiming at solving the two problems of large torque ripples and high computational complexity in conventional MPTC, the proposed method adopts a second optimization and a new simplified search strategy. The key idea of second optimization is to make the output voltage vector closer to the actual optimal solution. In this case, a more suitable voltage vector is applied in each sampling period. The simplified search strategy reduces the calculation time by cutting down the number of candidate voltage vectors without affecting drives performance. Compared to the conventional MPTC without DSVM and with DSVM, the proposed method can produce superior steady-state performance and lower computational complexity. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method.

A Non-Integer High-Order Sliding Mode Control of Induction Motor with Machine Learning-Based Speed Observer

The induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies.

Model Predictive Torque Control-Based Induction Motor Drive with Remote Control and Monitoring Interface for Electric Vehicles

While the increased use of internal combustion engine vehicles raises carbon emissions, electric vehicles (EVs) are becoming more important for reducing air pollution. EV production costs are decreasing as technology advances since they are typically powered by alternating current induction machines (ACIM). In EV configurations that are propelled with ACIM, an inverter is required to obtain appropriate voltage levels to drive the IM. Multilevel inverters are widely preferred in the industry for DC–AC voltage conversion due to their high-power quality. In this study, the ACIM is supplied with the appropriate voltage using an active neutral point clamped (ANPC) inverter. The ANPC inverter acting as ACIM driver is controlled with model predictive torque control. The simulation of the proposed ACIM drive has been performed with torque and speed control features to determine the operational features of the experimental test bench. Furthermore, the designed ACIM drive includes remote control and monitoring functions via an Internet of Things-based module. This allows for instant monitoring of the input, output, and current values of the ANPC inverter. The graphical monitoring function is used to track the speed and torque values of ACIM while the speed can be changed remotely via the web interface.

An Improved Model Predictive Torque Control for Switched Reluctance Motors With Candidate Voltage Vectors Optimization

This paper proposes an improved model predictive torque control (MPTC) strategy for switched reluctance motor (SRM) drives with candidate voltage vectors (CVVs) optimization, which can suppress the torque ripple effectively and enhance the system efficiency. This MPTC method is improved by three aspects. Firstly, the flux linkage calculation is removed compared to conventional MPTC. Secondly, the electric cycle of SRM is divided into six sectors based on the ideal torque contribution profile and the commutation region of the motor is redefined. Finally, CVVs of each sector are adopted based on phase torque characteristics and the total number of CVVs is reduced to 2 or 3 at each control period. The cost function is designed for the torque ripple suppression and copper loss minimization by selecting the optimal voltage vector from CVVs. This improved MPTC method avoids mass useless computation compared to conventional MPTC and no longer relies on hysteresis control loops compared to DTC method. In the simulation and experimental verification on a three-phase 12/8 SRM, the proposed MPTC method effectively reduces the torque ripple, improves the torque–ampere ratio and system efficiency, and improves dynamic response compared to DTC method.