Model-based Predictive Current Control Research Articles

Model-Free Predictive Current and Disturbance Rejection Control of Dual Three-Phase PMSM Drives Using Optimal Virtual Vector Modulation

Model-based predictive current control (MBPCC) relies heavily on adequate system modeling and accurate parameters. The detailed models of dual three-phase permanent magnet synchronous motors (PMSMs) contain mutual cross-coupling dynamics which make it difficult to extract accurate parameters. Also, some of the physical parameters of PMSMs are generally nonlinear functions of current and rotor position. In this article, a model-free predictive current control (MFPCC) based on an ultralocal model and an extended state observer is proposed for an asymmetrical dual three-phase (ADTP) PMSM. The MFPCC method is shown to provide superior current regulation when compared to the standard MBPCC approach under uncertain parameter conditions. Furthermore, the harmonic currents and the current ripple in the ADTP PMSM drive have been regulated to near-zero values by using optimized voltage vectors comprised of virtual vectors and null vectors. A generalized center-aligned pulsewidth modulation (PWM) scheme is presented to facilitate the synthesis of the optimal virtual voltage vectors for implementation on a low-cost digital signal processing platform. The performance improvements of the MFPCC with optimal virtual vector modulation are verified and compared with the conventional MBPCC method in both simulations and experiments on a surface-mount type dual three-phase PMSM.

Integration of Reference Current Slope Based Model-Free Predictive Control in Modulated PMSM Drives

Conventional model-based predictive current control (MBPCC) suffers from high current ripple and system parameter dependence problem. To address these issues, a novel reference current slope-based modulated model-free predictive control (MFPC) is proposed in this article. In the proposed method, a reference current slope is obtained based on the current deadbeat solution. A reduced amount of active voltage vectors can then be determined in a straightforward way according to the reference current slope and the current slopes for the possible voltage vectors, thus avoiding enumerating operation in the conventional MBPCC. To reduce the current ripples, a two-vector modulation strategy is introduced to the proposed method. In addition, the current slopes for the possible voltage vectors are obtained in a model-free manner based on online measured data only to reduce the influence of parameter uncertainties on the controller. Here, the principles of the two-vector modulation are well-considered, and the current variations with the input voltage vectors from two sampling time intervals are used to estimate the current slopes for all the voltage vectors. As a result, even though two voltage vectors with variable time durations are applied in each control period, the current slope information can be refreshed every half control period, thus guaranteeing the reliability and accuracy of current predictions. The proposed method is verified on a permanent magnet synchronous machine (PMSM) setup to demonstrate its improved parameter robustness and steady-state performance.

An Improved Model−Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High−Gain Disturbance Observer

Predictive current control (PCC) is an advanced control strategy for permanent magnet synchronous motors (PMSM). When the motor drive system is undisturbed, predictive current control exhibits a good dynamic response speed and steady−state performance, but the conventional PCC control performance of PMSM that depends on the motor body model is vulnerable to parameter perturbation. Aiming at this problem, an improved model−free predictive current control (IMFPCC) strategy based on a high−gain disturbance observer (HGDO) is proposed in this paper. The proposed strategy is introduced with the idea of model−free control, relying only on the system input and output to build an ultra−local current prediction model, which gets rid of the constraints of the motor body parameters. In the paper, the ultra−local structure is optimized by comparing and analyzing the equation of the state of the classical ultra−local structure and PMSM system. The system’s current state variables are incorporated into the ultra−local system modeling, as a result, the current estimation errors existing in the classical ultra−local structure are eliminated. For the unmodeled and parametric perturbation part of the ultra−local system, a high−gain disturbance observer is designed to estimate it in real time. Finally, the proposed IMFPCC strategy is compared with the conventional model−based predictive current control (MPCC) and the conventional model−free predictive current control (CMFPCC) in simulation and experiment. The results show that the current steady−state error of the IMFPCC strategy in the case of parameter variation is only 50% of the MPCC method, which proves the effectiveness and correctness of the proposed strategy.

Model Free Predictive Current Control Based on a Grey Wolf Optimizer for Synchronous Reluctance Motors

A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results.

Model-Free Predictive Current Control of Synchronous Reluctance Motors Based on a Recurrent Neural Network

Recently, model-based predictive current control (MB-PCC) has been presented as a good alternative to classical control algorithms in terms of simplicity and performance reliability. However, MB-PCC suffers from the high dependence on system parameters, which may deteriorate its performance under parameters variations. On the other hand, synchronous reluctance motors (SynRMs) are susceptible to suffer from inductances variations due to the magnetic saturation. Accordingly, in this article a new model-free predictive current control of SynRMs based on a recurrent neural network (RNN-PCC) is developed and proposed. The proposed RNN-PCC relies on the identification of the SynRM currents without considering any parameters. Simulation and experimental results show that both RNN-PCC and MB-PCC have similarly excellent dynamics, while better control performance and tracking errors can be achieved thanks to the proposed RNN-PCC.

Model Predictive Current Control of Six-Phase Induction Motor Drives Using Virtual Vectors and Space Vector Modulation

The use of multiphase machines has become a suitable choice in high-performance industry applications through advantages such as lesser torque ripple, enhanced current distribution per phase, and fault-tolerance capability. Among different control approaches for the regulation of multiphase drives, model-based predictive current control (MPCC) is one of the most analyzed strategies due to its adaptability and good dynamic response. However, this approach presents some disadvantages, e.g., high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents and increased harmonic content in the fundamental <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha -\beta$</tex-math></inline-formula> stator currents. Modulation strategies have been combined with MPCC to overcome these shortcomings. This article proposes a modulated MPCC with virtual vectors and space vector modulation for the regulation of an asymmetrical six-phase induction machine to minimize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents, reduce the harmonic content, and perform improved stator currents tracking compared with other MPCC versions. Experimental tests are provided to demonstrate the quality of the proposed current control strategy.

Robust Predictive Stator Current Control Based on Prediction Error Compensation for a Doubly Fed Induction Generator Under Nonideal Grids

Traditional model-based predictive current control (MBPCC) for a doubly fed induction generator (DFIG) is easily affected by machine parameter variations. Recently, a model-free predictive current control (MFPCC) was proposed to enhance the parameter robustness of DFIG systems by using the past current difference to predict the future current difference under the same switching state. However, this approach has the problems of high sampling frequency, variable switching frequency, and high steady ripples. To solve these problems, this article proposes a robust predictive stator current control (RPCC) method for DFIGs. Different from the prior MFPCC, the current error between the measured value and the predicted value is not only used in the stage of the stator current prediction, but also in the calculation of rotor voltage vector. Hence, the performance deterioration caused by machine parameter variations is significantly alleviated. Furthermore, a support vector machine can be combined with the proposed RPCC. In this way, the proposed method ensures strong parameter robustness and small steady-state power ripples. Finally, by recalculating the current reference value, the method can also achieve a good performance even under nonideal grids. The proposed RPCC is compared with MBPCC and MFPCC and its effectiveness is confirmed by the presented simulation and experimental results.

Deadbeat Predictive Current Control for High-Speed Permanent Magnet Synchronous Machine Drives With Low Switching-To-Fundamental Frequency Ratios

This article analyzes the conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -frame model-based deadbeat predictive current control (DBPCC) methods for high-speed permanent magnet synchronous machine (PMSM) drives with low switching-to-fundamental frequency ratios (SFRs). It shows that the state-of-the-art compensation schemes of control delay and rotor movement effect can improve the control performance but the problem still arises at very high speeds with very low SFRs. Therefore, this article presents a novel DBPCC method for high-speed PMSM drives. The proposed method tracks the machine stator flux vector in the stationary frame to achieve deadbeat control of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents. The control delay and rotor movement effect are both precisely considered. Consequently, the control performance and stability of the proposed DBPCC can be guaranteed at high speeds. Extensive simulations and experiments have been performed on a prototype high-speed PMSM drive. The effectiveness of the proposed method and its superiorities against the field-oriented control and the conventional DBPCCs have all been demonstrated.

Model-Free Predictive Current Control of a PWM Rectifier Based on Space Vector Modulation Under Unbalanced and Distorted Grid Conditions

Among various control strategies for pulsewidth-modulated (PWM) rectifiers, model-based predictive current control (MBPCC) based on space vector modulation (SVM) is a simple and effective method due to its quick dynamic response and good steady-state performance. However, SVM-based MBPCC (SVM-MBPCC) suffers from parameter variations and grid disturbances, which are common in practical applications due to temperature effects, saturation, grid faults, and so on. Recently, model-free predictive current control (MFPCC) based on current differences has been proposed to address the problem of parameter robustness. However, this approach suffers from stagnant updating of the current differences, and the steady-state ripples are still high due to the application of a single vector in one control period. This article proposes an SVM-MFPCC, which combines the merits of both SVM-MBPCC and MFPCC. The proposed method uses an ultralocal model rather than a conventional accurate model of the PWM rectifier, which is updated online based on the voltages and currents in past control periods. By using this ultralocal model, a voltage reference to nullify the current error can be calculated based on the principle of deadbeat control and is subsequently synthesized via SVM. Furthermore, by adding an appropriate compensation power to the original power references, the proposed method can achieve sinusoidal and balanced grid currents even under unbalanced and distorted grid conditions. The proposed method is compared to conventional SVM-MBPCC under various grid conditions, and the results of simulations and experiments confirm its effectiveness.

Hybrid Switching of Four-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SynRM Drive Systems

Conventional model-based predictive current control (MBPCC) suffers from common drawbacks of high reliance on system model parameters and the use of a single input voltage vector that result in large pulsating current ripples and prediction errors. In the case of a four-switch three-phase inverter (FSTPI) topology, the implementation of the predictive controller is exacerbated due to limited numbers of candidate voltage vectors. This paper presents an integrated model-free predictive current control with a hybrid switching mechanism to solve the problem. The proposed method introduces the combined switching mechanism of input voltage vectors with fixed and variable modulations by increasing the number of switching voltage vectors. The four basic voltage vectors generated in the four-switch three-phase inverter create twenty-four new synthesized voltage vectors through fourfold linear expansions of the space vector plane. The switching durations of input voltage vectors are determined by calculating their optimal duty ratios. As a result, the proposed method improved the prediction accuracy by increasing the iteration calculations of current differences every sampling period. The proposed method, known as the hybrid switching of four-voltage-vector model-free predictive current control (HS-4VV-MFPCC), is practically tested <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">via</i> simulation and experimental works to evaluate its effectiveness and performance improvement.

Robust predictive current control of PWM rectifiers with LCL filters under unbalanced and distorted network conditions

Pulse-width modulation (PWM) rectifiers with LCL filters can achieve better filtering and have lower total inductance than its counterpart of L filters. However, the control complexity is also increased due to the higher system order. Furthermore, the performance may deteriorate significantly when the inductance used in the controller deviates from its actual value due to saturation, temperature, and so on. This paper proposes a robust predictive current control (RPCC) scheme based on the ultra-local model for LCL-filtered PWM rectifiers. Excluding the converter voltage related term, the other part of the system F including the disturbance caused by parameter mismatches and other dynamic process, can be estimated using differential algebra (DA) or an extended state observer (ESO). After obtaining F, the reference voltage vector can be calculated based on the principle of deadbeat current control. The proposed RPCC is extended to an unbalanced and distorted grid by modifying the grid current reference; thus, the three-phase grid currents are balanced and sinusoidal. The proposed RPCC based on DA and ESO is compared to the conventional model-based predictive current control (MPCC), and the experimental results confirm its robustness and effectiveness under mismatched parameters and network disturbances.

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

The development of new control techniques for multiphase induction machines (IMs) has become a point of great interest to exploit the advantages of these machines compared to three-phase topology, for example, the reduced phase currents and lower harmonic contents. One of the most analyzed techniques is the model-based predictive current control (MPC) with a finite control set. This technique presents high x–y currents because of the application of one switching state throughout the whole sampling period. Nevertheless, it is one of the most used due to its excellent dynamic response. To overcome the aforementioned drawbacks, new techniques called virtual vectors have been developed, but although there are several articles with experimental results, the algorithm for implementing the technique has not been appropriately described. This document provides a clear and detailed explanation for algorithm implementation of virtual vectors through two proposed variants VV4 and VV11, in a six-phase machine drive. The first entails lower computational cost and the second lower loss in the x–y plane. According to performance indicators such as the total harmonic distortion and the mean square error for both case studies, experimental tests were evaluated to determine the implementation’s behaviour.

Model Predictive Control for PMSG-Based Wind Turbines With Overmodulation and Adjustable Dynamic Response Time

This article presents an improved model-based predictive current control for a surface permanent magnet synchronous generator (SPMSG), where the dynamic response time can be adjusted according to the characteristics of the application and that can operate in the overmodulation region. Moreover, the steady-state errors typically observed in other model predictive controls (MPCs) due to the uncertainties of the system and errors in the parameter values are removed in the proposed MPC. The proposed MPC was tested for the nED100 wind turbine of Norvento in MATLAB/Simulink environment. Besides, it was assessed in an 8.7 kW SPMSG laboratory prototype. The simulation and experimental results indicate that the proposed MPC works properly in an overmodulation region and the desired dynamic response time is achieved whereas the switching frequency is constant.

Model-Based Predictive Rotor Current Control Strategy for Indirect Power Control of a DFIM Driven by an Indirect Matrix Converter

This paper presents a new control strategy using model-based predictive current control (MB-PCC) for a doubly fed induction machine (DFIM) driven by an indirect matrix converter (IMC). This strategy proposes the control of rotor currents, whose references are calculated from active and reactive stator power set points and the dynamic model of the DFIM. The control strategy works well in the four P-Q operating regions of the DFIM. The grid synchronization process is carried out by setting the P-Q power set points to zero. The results include the DFIM synchronization procedure as well as the active and reactive power control at variable shaft speed to validate the feasibility of the proposed strategy.

Robust Digital Nonlinear Control System for Dual Active Bridge (DAB) DC/DC Converters With Asymmetric Half-Cycle Modulation

This article presents a new digital control system for dual active bridge dc/dc converters. The proposed control system includes a novel compensator with asymmetric half-cycle modulation. The proposed compensator is based on a novel geometric-sequence-control algorithm, which is a model-based predictive current control. The proposed control system provides fast and seamless tracking of the reference current and ensures zero dc current in the transformer windings (prevents transformer saturation). Moreover, it offers high robustness against perturbations and uncertainties in the converter. It also allows symmetric growth of transformer current during transients, thereby eliminating potential temporary saturation issues. Analysis, simulation, and experimental results are provided to demonstrate the superior performance of the proposed control in achieving high precision output regulation, fast and seamless transient response, and high robustness.

Optimal voltage vector in current control of PMSM considering torque ripple and reduction of the number of switching operations

This study presents a model predictive-based current control for permanent magnet synchronous motors (PMSMs). In the authors' proposed approach, a reference voltage to an inverter can be strictly treated as a discrete voltage vector so that the output saturation of the inverter is avoided. Various constraints can be also taken into consideration in their approach. Designing the band of frequency is incorporated into a single objective function. The effectiveness of the proposed method is verified through simulations and experiments.

Low cogging torque design and simplified model-free predictive current control for radial-flux dual three-phase permanent magnet electric machines

This study presents two improved designs of eccentric-shaped permanent magnets and teeth-shaped stators in radial-flux dual three-phase permanent magnet electric machines to reduce cogging torque and torque ripple. The finite element analysis (ANSYS Electromagnetics) has been adopted in simulation, and real radial-flux dual three-phase permanent magnet electric machines have been fabricated in experiment to verify the study. Using the radial-flux dual three-phase permanent magnet electric machines in electric machine systems can improve the reliability and obtain higher output torque. In electrical drives and control, a simplified model-free predictive current control method has been proposed and implemented to drive the radial-flux dual three-phase permanent magnet electric machines, and the control law has been achieved by a TMS320F28377S microcontroller of Texas Instruments. The simplified model-free predictive current control method is senseless to parameter variations and back electromotive force of the permanent magnet electric machines, and only needs current sensors to detect six-phase current. The optimal one has been chosen by 14 various switching modes, which has the minimum cost function, and then the converter can be directly driven and controlled in the next sampling period. The features of the simplified model-free predictive current control method can reduce the algorithm calculation and avoid the defect of conventional model-based predictive current control scheme. A proportional-integral speed controller has also been designed to achieve the speed response of the fixed-speed tracking effect. Finally, the feasibility and effectiveness of the proposed simplified model-free predictive current control method for the dual three-phase permanent magnet electric machines can be verified in the experimental and quantitative results.

Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control

Recently, low-frequency oscillation (LFO) has occurred many times in high-speed railways and has led to traction blockades. Some of the literature has found that the stability of the vehicle-grid coupling system could be improved by optimizing the control strategy of the traction line-side converter (LSC) to some extent. In this paper, a model-based predictive current control (MBPCC) approach based on continuous control set in the dq reference frame for the traction LSC for electric multiple units (EMUs) is proposed. First, the mathematical predictive model of one traction LSC is deduced by discretizing the state equation on the alternating current (AC) side. Then, the optimal control variables are calculated by solving the performance function, which involves the difference between the predicted and reference value of the current, as well as the variations of the control voltage. Finally, combined with bipolar sinusoidal pulse width modulation (SPWM), the whole control algorithm based on MBPCC is formed. The simulation models of EMUs’ dual traction LSCs are built in MATLAB/SIMULINK to verify the superior dynamic and static performance, by comparing them with traditional transient direct current control (TDCC). A whole dSPACE semi-physical platform is established to demonstrate the feasibility and effectiveness of MBPCC in real applications. In addition, the simulations of multi-EMUs accessed in the vehicle-grid coupling system are carried out to verify the suppressing effect on LFO. Finally, to find the impact of external parameters (the equivalent leakage inductance of vehicle transformer, the distance to the power supply, and load resistance) on MBPCC’s performance, the sensitivity analysis of these parameters is performed. Results indicate that these three parameters have a tiny impact on the proposed method but a significant influence on the performance of TDCC. Both oscillation pattern and oscillation peak under TDCC can be easily influenced when these parameters change.

Model-Based Predictive Current Control Method with Constant Switching Frequency for Single-Phase Voltage Source Inverters

Voltage source inverters operated by predictive control methods generally lead to a variable switching frequency, because predictive control methods generate switching operation based on an optimal voltage state selected at every sampling period. Varying switching frequencies make it difficult to design output filters of voltage source inverters. This paper proposes a predictive control algorithm with a constant switching frequency for the load current control of single-phase voltage source inverters. This method selects two future optimal voltage states used in the subsequent sampling period, which are a zero-voltage state and a future optimal voltage state, based on the slope of the reference current at each sampling period. After selecting the two future voltages, the proposed method distributes them to produce a constant switching frequency and symmetric switching pattern. The performance of the proposed method is validated with both simulation and experimental results for single-phase voltage source inverters.

Model‐free predictive current control for three‐phase AC/DC converters

A novel model-free predictive current control (MFPCC) approach for three-phase AC/DC converters is proposed, which only measures the input currents and calculates their differences under different switching states. Existing model-based predictive current control (MBPCC) methods are based on converter models and thus require the knowledge of input current, input and terminal voltages, resistance, and inductance. Compared to the MBPCC, the main advantages of the proposed MFPCC include eliminations of the converter model, multiplication operations, and tuning of system parameters. Simulation results show that the proposed MFPCC controls the input current slightly better than does the MBPCC. Experimental results are provided to validate the effectiveness of the proposed method, which are obtained from a three-phase AC/DC converter under the specifications of 380 V output voltage, 220 V/AC input voltage, and 1000 W output power. The measured harmonics orders 2-19 are shown to meet the IEC61000-3-2 Class A standard under 1000 W of output power. To the authors' best knowledge, it is the first MFPCC for such applications.