Model Predictive Torque Control Research Articles

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.

A Low-Complexity Modulated Model Predictive Torque and Flux Control Strategy for PMSM Drives Without Weighting Factor

This article proposes a modulated model predictive torque and flux control (M2PTFC) method with low complexity for a two-level voltage source inverter (2L-VSI)-fed permanent magnet synchronous motor (PMSM). The proposed strategy aims to reduce the computation burden and simplify the control implementation of the conventional M2PTFC scheme by reducing the number of candidate voltage vectors at every control sample to the minimum (i.e., only two candidates) and eliminating the weighting factor of the cost function and its corresponding tuning procedures. For these purposes, the proposed method devotes different control objectives to the duty modulation and the cost function evaluation processes while executing them sequentially. First, aiming at torque ripple reduction, a duty modulation strategy is proposed based on the analysis of torque deviations produced by different voltage vectors, which ensures proper selection of the two candidates and restricts active voltage vectors (AVVs) that cause high deviations. Then, these candidates are evaluated based on a cost function assigned to achieve the stator flux control objective. The effectiveness of the proposed control scheme is verified through comparative assessment with the conventional M2PTFC and two existing simplified methods by means of simulation and experimental results.

A Novel Model Predictive Torque Control of SRMs With Low Measurement Effort

This article presents a low-measurement effort and less storage space but an effective method to reduce the torque ripple. First, a torque-balanced measurement method is presented to obtain the flux-linkage characteristics at four torque-balanced positions. Then, a four-order Fourier series model is proposed to describe the entire flux-linkage and torque characteristics. To reduce the storage space, a Polynomial-Fourier series model is proposed to describe the torque and current model from the rotor position and flux-linkage. Based on the proposed Polynomial-Fourier series model, a novel model predictive torque control (MPTC) is implemented to minimize the torque ripple with flux-linkage-based torque estimation. Experimental results show the proposed method can effectively reduce torque ripple with lower measurement effort and less storage space compared with the traditional MPTC method. Furthermore, the key issues of model predictive control, such as error analysis, weight coefficients selection, and magnetic saturation, are discussed in detail. The proposed method provides a low-effort and convenient way to implement the advanced control of SRMs in the industry application.

A Simplified Model Predictive Voltage Control for Three-Phase Four-Switch Inverter-Fed PMSM Drives With Capacitor Voltage Offset Suppression and Current Ripple Reduction

This article proposes a simplified model predictive voltage control (SMPVC) method without weighting factors for three-phase four-switch inverter-fed permanent magnet synchronous motor drives to suppress the capacitor voltage offset and reduce the current ripple. In this article, the control of electromagnetic torque and magnetic flux in the model predictive torque control is transformed into the control of the voltage vector, and the capacitor voltage offset is suppressed by injecting dc component obtained by an adaptive notch filter into the phase current. On the basis, the voltage vector is taken as the only control term in cost function without the weighting factors to suppress capacitor voltage offset, thus simplifying the calculation amount in cost function. Then the simplified voltage vector synthesis method is presented to synthesize the effective voltage vector to reduce the current ripple. Using the simple sector division, the switching sequence of the voltage vector can be determined quickly. The effectiveness of the proposed SMPVC method is validated by the simulation and experiment.

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

Three Voltage Vector Duty Cycle Optimization Strategy of the Permanent Magnet Synchronous Motor Driving System for New Energy Electric Vehicles Based on Finite Set Model Predictive Control

Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy electric vehicles and in other fields because of their simple structure, light weight, small size, and high power density. With the continuous advancement of production technology, the requirements of accuracy, rapidity, and stability in permanent magnet synchronous motor systems have gradually increased. Among many advanced control technologies, this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion. This strategy involves the construction of a reference stator flux linkage vector based on the analytical relationship between electromagnetic torque, reference stator flux linkage amplitude, and rotor flux linkage and the transfer of the separate control of electromagnetic torque and flux linkage amplitude into flux linkage vector control. At the same time, the optimal duty cycle corresponding to the two adjacent extended voltage vectors and the zero vector is calculated according to geometric relationships so as to realize the three voltage vector duty cycle optimization control. Experimental results show the effectiveness and superiority of the proposed strategy.

Finite Control Set Model Predictive Torque Control With Reduced Computation Burden for PMSM Based on Discrete Space Vector Modulation

This paper proposes a finite control set model predictive torque control (FCS-MPTC) with reduced computation burden for permanent magnet synchronous motor (PMSM). Based on discrete space vector modulation (DSVM), the proposed DSVM-MPTC extends the control set. Therefore, compared to the traditional FCS-MPTC, it greatly reduces the torque ripples and current harmonics while maintaining a constant switching frequency. Furthermore, a new voltage vector optimization strategy is employed to decrease the number of candidate voltage vectors from 38 to 13, the proposed DSVM-MPTC significantly reduces the computation burden without compromising the control performance. The validity of the proposed DSVM-MPTC is verified by comparative experiments.

A look-up table-based model predictive torque control of IPMSM drives with duty cycle optimization

Model Predictive Control (MPC) is an effective method of driving motors and power converters due to its quick response, integrity, and multivariable control adaptability. A model-predictive torque control (MPTC) technique for permanent magnet synchronous motors (IPMSMs), which is computationally efficient and of low complexity, is presented in this paper. The proposed technique designs a lookup table that is independent of flux angle and torque deviation. For each control instant, this technique has to evaluate four voltage space vectors (VSV) from the lookup table, resulting in a substantial reduction in switching frequency and computational burden without compromising the performance. A maximum torque per ampere (MTPA) technique generates reference currents. The controller’s complexity is minimized by eliminating the flux weighting factor from the cost function, saving time on offline weighting factor adjustments. Moreover, duty cycle optimization is performed using the mean torque control technique to minimize torque and flux ripples. The proposed method has been experimentally validated using a real-time simulator hardware in loop (HIL) with a TMS320F28335 floating-point digital signal processor on a prototype IPMSM drive. Furthermore, the proposed MPTC scheme is compared to conventional MPTC and direct torque control (DTC).

Virtual Three-Level Model Predictive Flux Control With Reduced Computational Burden and Switching Frequency for Induction Motors

Model predictive flux control (MPFC) is an alternative control solution to the classic model predictive torque control (MPTC) for driving induction motors (IMs) without the complexity of tuning the weighting factors. However, when applied in motor drives fed by a two-level converter, it results in higher flux and torque ripples. To improve its performance, this article proposes a virtual three-level MPFC (V3-MPFC) method. The virtual vector method increases the total number of voltage vectors crucial for improving the closed-loop performances of the model predictive control. To reduce the computational burden, direct torque control and novel deadbeat (DB) control strategies are proposed. Compared with existing methods, the proposed DB-based method reduces V3-MPFC to a suboptimization problem and still ensures that the suboptimal solution is equivalent to the global optimal solution provided by V3-MPFC. In addition, the redundant states are exploited to lower the switching frequency (SF) without extending the cost function. The simulation and experiment results show that, compared with existing methods, the proposed strategy effectively reduces the flux and torque ripples, and requires a lower SF and execution time.

Reducing Air Pollutant Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst warm-up in mild HEV via Cascaded MPC

In this paper, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.