Conventional Model Predictive Current Control Research Articles

Model Predictive Virtual Flux Control Method for Low Switching Loss Performance in Three-Phase AC/DC Pulse-width-Modulated Converters

Three-phase AC/DC pulse-width-modulated (PWM) converters have been widely employed in various renewable energy systems and industrial applications, which require a high-efficiency power converter operation. This article proposes a technique to reduce switching loss in AC/DC converters by integrating a voltage vector preselection strategy to model predictive virtual flux control. The voltage vector preselection strategy preselects available voltage vectors corresponding to switching states that lead to minimum switching loss in the phase leg, which conducts the highest current. By using preselected voltage vectors, clamping intervals are generated at every fundamental period to maintain the present switching states of the power switches, resulting in the reduction in switching loss. Additionally, by using virtual flux control, the proposed approach can effectively be used under both ideal and distorted source voltage conditions. The proposed method is compared with the conventional model predictive current control and the conventional model predictive virtual flux control. Both a simulation and experiment are performed to validate the correctness and effectiveness of the proposed method, which has been found to decrease the switching loss of an AC/DC converter by up to 15% compared to conventional control schemes at a negligible increase in the input current total harmonic distortion and DC output voltage ripple.

A Simplified Multivector-Based Model Predictive Current Control for PMSM With Enhanced Performance

To address the challenges of poor steady-state performance and large amount calculation of conventional model predictive current control (MPCC), a simplified multi-vector-based MPCC with enhanced performance is proposed in this paper. Firstly, an effective voltage vectors (VVs) selection method based on current-error is proposed, which can directly determine the optimal VVs without cost function enumeration. Compared with the standard method, the number of vectors that need to be evaluated is reduced from 7 to 1. Meanwhile, the duty cycle of each VV is calculated based on the current error to realize error-free control. In addition, to alleviate the deleterious effect of dead-time on the control performance, an improved MPCC scheme with optimal utilization of dead-time is proposed. The key is that the dead-time existing in MPCC is regarded as a dead-time voltage vector (DVV), which can be rationally utilized to improve the control performance. Both theoretical analysis and experimental results are given to verify the effectiveness of the proposed MPCC schemes.

Effective Multivector-Operated Predictive Current Control of PMSM Drive With Reduced Torque and Flux Ripple

Model predictive current control (MPCC) is a frequently used method for the control of permanent magnet synchronous motor (PMSM). It has faster execution on modern digital platform than the previous generation micro-controllers. Conventional MPCC fails to provide satisfactory steady-state performance as a single voltage vector (VV) is applied during every sampling interval. This article proposes two novel multi-vector operated MPCC methods for reducing torque and flux ripples, as well as the computational burden. The first proposed method uses one active and one null VV; whereas, the second method uses two active and one null VV to further improve the steady-state performance. Proposed methods determine required change in stator-flux (CSF) to reduce/eliminate error in statorcurrent during every sample and VVs nearer to CSF vector are chosen as optimal VVs. The durations of optimal VVs are calculated using the components of CSF along optimal VVs. CSF, obtained using calculated durations of optimal VVs, significantly reduces torque and flux ripples. The proposed methods are effective and less complex compared to other multi-vector operated methods. The proposed methods are compared with existing two-VV and three-VV based MPCC methods and their effectiveness is experimentally verified for decreased torque and stator-flux ripple in addition to a small rise in switching frequency.

Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

Abstract The four-level active neutral point clamped (ANPC) inverter has become increasingly widely used in the renewable energy industry since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter. The model predictive current control (MPCC) is a promising control method for multi-level inverters. However, the conventional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications. A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper. The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance. The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing. The efficacy of the proposed MPCC is verified by simulation and experimental results.

Model Predictive Current Control for Asymmetric Six-phase Permanent Magnet Motors With Less Computation and Optimized Voltage Vector Magnitude

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy.

Simplified Model Predictive Current Control of Four-Level Nested Neutral Point Clamped Converter

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

A Multivector-Based Model Predictive Current Control of PMSM Drive With Enhanced Torque and Flux Response

Model predictive current control (MPCC) is a modern current control technique with the provision to include multivariable objectives and nonlinearities. In this article, a multivector-based MPCC is proposed for the speed control of a two-level voltage source inverter (VSI) fed permanent magnet synchronous motor (PMSM) drive, to improve the steady-state (SS) torque and flux response. The multivector operation is obtained using an extended control set (ECS) that utilizes two adjacent-active voltage-space vectors (VSVs) with an appropriate distribution coefficient. However, the augmentation of the control set (CS) catalyzes an undesirable increase in the computational burden. To address this limitation, a lookup table-based VSV preselection scheme based on stator current error is employed. The VSV grouping is based on the gradient of stator current error and effectively replicates the control scheme with all ECS-VSVs. Subsequently, the amplitude of the selected optimum ECS-VSV is optimized using an average error minimization technique. The optimum duration of the ECS-VSV is determined without employing space vector modulation (SVM). Hence, the complexity of the proposed scheme is minimized, and the SS torque and flux performance are enhanced without compromising the dynamic performance of the drive. The effectiveness of the proposed scheme is verified by conducting comprehensive comparisons with conventional MPCC and recently published literature.

Elimination of Dead Time Effects on Common Mode Voltage in an Open-End Winding Induction Motor Drive Under Low Speed Operation Using a Simplified Model Predictive Control

In this article, a simplified model predictive current control (MPCC) for a three-level open-end winding induction motor drive mitigating the dead time effects on common mode voltage (CMV) in the low speed operation of the motor is proposed. The traditional current based objective function is replaced with an equivalent voltage based objective function, and an additional term indicating the nonzero CMV during dead time is added to achieve complete CMV elimination. Further, a sector-based selection of candidate switching states is employed to reduce the computational burden. It is also shown that the constraint on eliminating the dead time effect on CMV heavily deteriorates the performance of the drive at higher speeds and a smooth transition with and without the constraint is also demonstrated. The envisaged MPCC is simulated in MATLAB and is experimentally verified using TMS320F28377S launchpad. The proposed MPCC gives a massive reduction in the computational burden compared to the conventional MPCC without sacrificing the motor drive performance.

A Smith Structure-Based Delay Compensation Method for Model Predictive Current Control of PMSM System

In model predictive control (MPC) system for permanent magnet synchronous motor (PMSM), calculation delay of the strategy influences control performances seriously. A Smith structure-based model predictive current control (MPCC) strategy is proposed in this article. The cost function of MPCC is improved and the Smith structure is added to compensate calculation delay. Advantages such as speed ITAE, settling time, and stability compared with the conventional MPCC strategy under the same conditions are analyzed. The effectiveness and correctness of the proposed method are corroborated by the simulation and experimental results.

Enhanced Predictive Current Control of PMSM Drive With Virtual Voltage Space Vectors

Model predictive current control (MPCC) is commonly used method for the control of permanent magnet synchronous motor (PMSM) drive. MPCC is simple to implement and faster to execute on digital platform. The classical cost function in MPCC does not contain any weighting factor thereby resulting in a simpler cost function for the selection of optimal voltage space vector (VSV). Conventional MPCC (C-MPCC) applied to PMSM results in higher torque ripples. The ripples in the torque and stator flux can be reduced by reducing the duration of application of active VSV. This article proposes an MPCC based on virtual voltage space vector (VVSV) for reducing the torque ripples and improve the THD in stator current without affecting dynamic performance. Duty ratio for VVSV is chosen proportional to commanded speed to reduce change in stator current thereby reducing ripple in torque stator flux. The VVSVs have magnitude near to developed back-EMF space vector. Proposed MPCC is compared with C-MPCC and two other MPCC based on VVSV. Effectiveness of proposed MPCC is successfully verified after comparing the results of experimentation at different speed and load conditions. Proposed MPCC fails to reduce ripple in torque and stator flux above base speed and proposed scheme collapses into C-MPCC.

Improved Model Predictive Current Control of NPC Three-Level Converter Fed PMSM System for Neutral Point Potential Imbalance Suppression

For the neutral point clamped (NPC) three-level converter fed permanent magnet synchronous motor (PMSM) system, the performance of the conventional model predictive current control (MPCC) algorithm will be deteriorated if the amplitude of the neutral point potential (NPP) is large. Additionally, the adjustment process of the weighted coefficients of the conventional MPCC algorithm is complex because of numerous control terms in the cost function. To solve the above issues, an improved MPCC algorithm is proposed in this paper. Firstly, Newtonian iteration is used to transfer the stator current into stator voltage in the cost function. Then, the NPP term in the conventional cost function can be eliminated by introducing the partition control of the NP potential, which also eliminates the whole adjustment process of weighting coefficients. Finally, based on the amplitude of the NPP, the amplitude and phase angle of medium and small vectors are modified to improve the control performance of the torque and flux. Experimental results show that the fluctuation of the neutral point potential can be suppressed rapidly. Meanwhile, the performance of the torque, flux and current are also improved compared with the conventional MPCC.

A Novel Single-Phase Shunt Active Power Filter with a Cost Function Based Model Predictive Current Control Technique

For a single-phase Shunt Active Power Filter (SAPF) with a two-step prediction, this research presents a modified current control based on a Model Predictive Current Control (MPCC) technique. An H-bridge inverter, a DC link capacitor, and a filter inductor comprise the single-phase SAPF topology. The SAPF reference current is computed using the DC-link capacitor voltage regulation-based PI control technique. The weighting factor-based model predictive current controller is used to track the current commands. The essential dynamic index for evaluating waveform quality is the Total Harmonic Distortion (THD) of a source current and switching frequency of power switches. The conventional methods the THD and switching frequency are not considered as an objective function, so that a weighting factor-based MPCC technique is used to obtain a good compromise between the THD of the source current and switching frequency of power switches. Through MATLAB simulation and experimentation with the Cyclone-IV EP4CE30F484 FPGA board, the usefulness of the proposed control technique is proven. As compared with hysteresis, predictive PWM, and conventional MPCC control methods, the cost function-based MPCC algorithm provides a lower switching frequency (13.4 kHz) with an optimal source current THD value.

Computational efficient model predictive current control for interior permanent magnet synchronous motor drives

The standard model predictive control (MPC) of three-phase motors requires heavy calculation efforts for evaluating all voltage vectors (VV) in addition to the variable switching frequency, large current harmonics, and torque ripples. To deal with these problems, a computational efficient model predictive current control (MPCC) is proposed for a three-phase interior permanent magnet synchronous motor (IPMSM). Initially, to avoid the protracted enumeration process, the reference voltage vector (RVV) is directly calculated by using the reference current generated by the maximum torque per ampere technique (MTPA), which is an additional control objective. The position of the RVV is utilized to define three candidate voltage vectors to be examined using the cost function, which determines the optimal vector. Secondly, an optimal duty cycle (ODC) is designed to minimize the error between the optimal vector and the reference vector reducing the current ripples. Furthermore, the proposed scheme is compared with the conventional MPCC techniques using MATLAB simulation and hardware in loop (HIL) with TMS320F28335 digital signal processor (DSP) experiments. A comprehensive analysis of the results for different operating conditions shows the effectiveness and robustness of the proposed method.

An Improved Model Free Predictive Current Control for PMSM With Current Prediction Error Variations

The conventional model predictive current control is a model-based control method, and the accuracy of the predicted currents is affected by motor parameters such as flux linkage, inductance, and resistance. To get rid of model parameters dependencies, a model-free predictive current control (MFCC) was proposed before, which can improve the parameter robustness without utilizing any knowledge of the initial motor parameters. However, the stagnant current update detection is one of the main problems that limit the current predictive performance. To solve this problem, a current prediction error model according to the contiguous instant current error variations is proposed to reconstruct the surface-permanent magnet synchronous motor (SPMSM) model in this paper. Afterwards, a novel MFCC method with the online parameter identification is developed. This method takes advantage of mathematical relationships in the current prediction error model, and the motor parameters can be updated within each period to improve prediction accuracy. Simulation and experimental results verify that this proposed MFCC method can significantly reduce the stagnation effect and improve MFCC performance under different parameter disturbances.

Model predictive current control with lower switching frequency for permanent magnet synchronous motor drives

The model predictive current control (MPCC) technique has been an effective alternative for motor drive applications requiring fast dynamic response. However, the conventional MPCC suffers from high switching losses due to high switching frequency of the inverter. This study proposes a low average switching frequency MPCC method based on boundary-constrained quadratically extrapolated current trajectory for a permanent magnet synchronous motor drive. First, a quadratic function-based current prediction model is constructed to obtain the current trajectory in the long-term future. Second, the boundary constraint is designed to obtain the prediction horizon which is changing in real-time in order to ensure the validity of the predicted current trajectory. Finally, the cost function established according to the value of the prediction horizon is used to reduce the average switching frequency. In comparison with the conventional MPCC method, the proposed method can significantly reduce the switching frequency of the inverter. In the meantime, fast dynamic response and high steady-state performance of the conventional MPCC method are preserved. Both simulation and experimental results of the proposed method and the conventional MPCC method are presented to verify the feasibility and advantages of the proposed method.

A Novel Strategy of Control Performance Improvement for Six-Phase Permanent Magnet Synchronous Hub Motor Drives of EVs Under New European Driving Cycle

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC).

Model-Free Predictive Current Control for Three-Level Inverter-Fed IPMSM With an Improved Current Difference Updating Technique

Parameter mismatches in conventional model predictive current control (MPCC) would worsen torque and THD performance. To eliminate the parameter dependence of MPCC, this letter proposes a model-free predictive current control (MFPCC) for a three-level inverter-fed interior permanent magnet synchronous motor (IPMSM). Firstly, to increase the current difference updating frequency in the diode neutral-point-clamped (NPC) three-level inverter, a synchronous updating method is proposed in a manner where the number of updated current differences during one sampling period is increased up to 6. Secondly, for the sake of computational burden alleviation, a candidate vector selection algorithm is introduced in such a way that the number of candidate vectors is reduced to only 7–9. Then, the problem of neutral-point voltage fluctuation which is inherent to the three-level NPC inverter is investigated, and a reasonable replacement method incorporating the redundancy characteristic of small vectors is developed. Finally, experimental results verify that the system robustness against mismatched parameters can be greatly enhanced by use of the proposed control scheme; meanwhile, steady-state performance and dynamic-state response of the system can be improved to some extent as well.

A novel Model Predictive Current Control of Interior Permanent Magnet Synchronous Motor powered by Three-phase Four-switch Voltage-source Inverter

In this study, a novel model-predictive-current-control (MPCC) is developed and implemented based on an interior permanent magnet synchronous motor(IPMSM) drives fed by three-phase four-switch voltage-source inverter (TPFS-VSI) with fewer switches and lower costs. Compared with the conventional MPCC method, the complicated weighting factors designing process is avoided and it can greatly reduce the d-q axes currents and three-phase current ripples. Finally, MATLAB/Simulink simulation results validate the effectiveness of the proposed method .

Robust Model Predictive Current Control Based on Inductance and Flux Linkage Extraction Algorithm

Two-vector model predictive current control (MPCC), which applies two vectors in a control cycle, has better steady state performance than conventional MPCC. However, two-vector MPCC needs to select two optimal vectors, calculate two current slopes and two vector working times, which have to use the inductance and the flux linkage parameter in the permanent magnet synchronous motor (PMSM) drives. Therefore, the control performance of two-vector MPCC is more influenced by the model parameter accuracy. Aiming at reducing parameter sensitivity of the two-vector MPCC method, a robust two-vector MPCC method is proposed, which can obtain accurate inductance and flux linkage information in real time based on the presented inductance extraction algorithm and the flux linkage extraction algorithm. Moreover, the proportional-integral regulator parameters of the extraction algorithms are theoretically deduced. The experiment results of the proposed method indicate that the satisfactory control performance can be achieved under the condition of the parameter mismatches.

Integral Sliding Mode-Based Model Predictive Current Control With Low Computational Amount for Three-Level Neutral-Point-Clamped Inverter-Fed PMSM Drives

This article proposes a novel integral sliding mode (ISM)-based model predictive current control (MPCC) strategy with low computational amount for three-level neutral-point-clamped (3L-NPC) inverter-fed PMSM drives. For conventional MPCC for 3L-NPC inverter, 27 voltage vectors are evaluated for prediction and calculation in each control cycle, therefore computational burden is a major hurdle for practical implementation of MPCC. With only calculation of reference voltage and determination of its sector position, a revised MPCC strategy with neutral point potential (NPP) balancing is proposed, which not only dramatically reduces the number of candidate voltage vectors involved in cost function calculation but also effectively suppresses NPP fluctuation of 3L-NPC inverter. To enhance MPCC robustness against motor parameter variations, the integral sliding mode (ISM) is embedded into MPCC for PMSM drives by enforcing sliding mode throughout the entire system response, and thus a novel ISM-based MPCC strategy is put forward. Furthermore, its robust stability is analyzed. The results of simulation experiments confirm that the proposed ISM-based MPCC strategy with NPP balancing for 3L-NPC inverter can enable PMSM drive system to have satisfactory control performance and strong robustness against motor parameter variations.