Predictive Current Control Research Articles

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.

Model predictive current control for maximum power point tracking of voltage source inverter based grid connected photovoltaic system

<p>Due to the high demand of grid connected photovoltaic systems, there is a need to track the maximum power point of the PV system. As the output of PV system is dc, there should be a converter, acting as medium between PV system and dc bus capacitor to track maximum power at all the loads. Usually boost converter is acting as medium between PV system and dc link capacitor as the duty cycle of the insulated-gate bipolar transistor in boost converter is in between 0 to 1 for maximum loads during maximum power point tracking (MPPT). To make PV system stable, the balance point is dc bus. If the dc bus voltage is constant, the system will be stable. Then the transfer power will just depend on current. For this purpose, the active current reference signal is to be generated by setting up the reference voltage across dc bus. Here to generate active reference current, PI controller is used and the reference voltage is taken according to the peak voltage of the inverter output voltage. The proposed control strategy was evaluated on a three-phase inverter linked to the grid and supplied by the PV system, which is working under varying irradiation conditions.</p>

A new floor function single‐carrier‐based modulated model predictive current control technique for single‐phase PUC5 inverter topology

SummaryIn this paper, a new floor function (F2) single‐carrier (SC)‐based modulated model predictive current control technique (M2PC2T) has been proposed by considering the conventional single‐phase five‐level packed U‐cell (PUC5) inverter topology. Generally, to regulate the output current, the finite control set‐based MPC technique without a modulator inherently operates with variable switching frequency, and it is unsuitable for industrial applications. To alleviate this, authors have implemented M2PC2T, which provides constant switching frequency of operation. In this technique, generally, the predictive algorithm has been integrated with any modulation stage. However, while the implementation of pulse width modulation (PWM) techniques, most authors have used conventional PWM techniques which further increases the control complexity as the level number increases. In this manuscript, a simple and generalized F2‐SC‐PWM technique has been proposed and integrated with the predictive algorithm. This PWM technique implementation process is very simple as compared with all conventional PWM techniques since even if the level number increases, the number of modulating and carrier signals is always only one. Finally, the complete closed‐loop control technique is called a generalized F2‐SC‐M2PC2T which is suitable for all stiff direct current (dc) sources and switched capacitor‐type multilevel inverter (MLI) topologies. The proposed control technique (PCT) has been experimentally verified with different transient and steady‐state case studies by considering resistive‐inductive load.

A new single modulating and single carrier‐based predictive current control technique for single‐phase quadruple boost multilevel inverter topology

SummaryA new floor function single carrier‐based modulated model predictive current control (F2‐SCM2PC2) technique has been proposed in this paper by considering traditional single‐phase quadruple boost switched capacitor‐based multilevel inverter (QB‐SCMLI) topology. Conventionally, the implementation process of single modulating and single carrier‐based pulse width modulation (PWM) techniques are very much complex and finite control set‐based model predictive control (FCS‐MPC) technique inherently operates with variable switching frequency (VSF) operation, which is not suitable for industrial applications. To alleviate the aforementioned issues, a new generalized closed‐loop F2‐SCM2PC2 technique has been proposed. It consists of a simple PWM implementation process and can easily be integrated with a predictive algorithm, thereby providing a constant switching frequency (CSF) of operation. The particular single modulating and single carrier‐based PWM technique with floor function is also suitable for low and high switching frequency (LSF‐HSF) operations. The validation of floor function‐based PWM technique in open‐loop and closed‐loop F2‐SCM2PC2 techniques has been effectively verified through experimentation on QB‐SCMLI topology with consideration of resistive–inductive load.

Simplified Model Predictive Current Control for Surface- Mounted Permanent-Magnet Synchronous Motor Drives with Adaptive Duty Modulation

Parameter control and cost minimization are among the significant aspects of model predictive current controllers. However, with the conventional control scheme of fixed switching vector actuation, susceptibility to uncontrolled current ripples remains a primary concern. This paper presents a simplified approach of model predictive current controller baed on adaptive duty modulation for the surface-mounted permanent-magnet synchronous motor (SPMSM). In this method, the implementation of two successive synthesized voltage vectors adopts the adpative soft-switching combination in each control period. Experimental results validate performance improvement and optimize current predictive accuracy.

A Review on Predictive Control Technology for Switched Reluctance Motor System

The significance of employing control strategies on a switched reluctance motor (SRM) is that they can reduce vibration noise and torque ripple. With the rapid development of digital system processors, predictive control (PC), as a modern control approach, is increasingly applied to enhance the dynamic performance and operational efficiency of SRMs. This review provides a comprehensive overview of the current state of research on PC strategies of SRMs and classifies PC technologies, such as generalized predictive control (GPC), hysteresis predictive control (HPC), deadbeat predictive control (DPC), and model predictive control (MPC). It summarizes the PC schemes from the aspects of predictive current control (PCC), predictive torque control (PTC), and other PC, and it discusses the current trends in technology development, as well as potential research directions. The insights presented herein aim to facilitate further investigations into predictive control techniques for SRM.

DC‐link voltage control of small capacitor PMSM drive system based on MPC algorithm

SummaryThe permanent magnet synchronous motor (PMSM) drive system with small dc‐link capacitor will have the problem of unstable drive system due to the small damping of the LC filter and the negative impedance characteristics of the constant power load. The input LC filter can suppress interference and is essential for the normal operation of the drive, but it may not match the impedance of the post‐stage inverter, causing the drive system to become unstable. In this paper, by constructing the dc‐link voltage prediction model, combined with the PMSM current predictive control, the weighted cost function of the model predictive control (MPC) strategy is designed to realize the motor optimal control and the dc‐link voltage fluctuation suppression control. To avoid introducing additional sensors when constructing the dc‐link voltage model, a source state observer that can estimate the dc‐link voltage and input current is constructed. Simulation and experimental results are provided to verify the effectiveness of the proposed method.

Model predictive and SoC balancing control of a CHB inverter in battery energy storage application using reward functions

SummaryThis article presents an improved model predictive current control algorithm combined with a novel state of charge (SoC) balancing approach for a three‐phase cascaded H‐bridge inverter in battery energy storage applications. Based on the model predictive control with elimination of redundant voltage vectors, neighboring vectors of the last voltage vectors are used to find the optimal ones in only one control cycle, which solves the problems of high computational burden and slow dynamic response. To overcome the unsuitability of the conventional cost function in SoC balancing, the reward functions that effectively balance the inter‐phase SoCs, inner‐phase SoCs, and inner‐phase dc voltages are presented respectively. Simulation and hardware‐in‐the‐loop‐based experimental results validate the effectiveness of the proposed strategy.

Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems

With an emphasis placed on a low-carbon economy, photovoltaic grid-connected inverters are moving toward the center of the stage. In order to address the problems related to the strong parameter dependence of the conventional model’s predictive control in grid-connected inverters, an improved parameter-free predictive current control is proposed. Relying on an extended state observer, an ultralocal model is employed to predict future currents without any parameters. The system can achieve a satisfactory performance in terms of dynamic response and robustness. Additionally, 30 virtual voltage vectors are extended for lower current ripples, which is followed by the use of a triangle candidate strategy to significantly ease the computing burden. In general, the proposed strategy omits parameter dependence, complex tuning work, and large tracking errors. The effectiveness of the proposed model is verified through the experimental results.

Model Predictive Current Control Strategy for Improved Dynamic Response in Cascaded H-Bridge Multilevel Inverters

Model Predictive Current Control Strategy for Improved Dynamic Response in Cascaded H-Bridge Multilevel Inverters

Predictive Current Control for Three-Level Four-Leg Indirect Matrix Converter Under Unbalanced Input Voltage

In this paper, a robustness evaluation of model predictive current control with instantaneous reactive power minimization for a three-level four-leg indirect matrix converter is presented. Unbalanced voltages can be extremely dangerous, especially for motors and other inductive equipment. Unbalanced voltages can have a detrimental effect on equipment and the power system, which is exacerbated by the fact that a small phase voltage imbalance can result in a disproportionately large phase current imbalance. The robustness test is carried out by considering balance and unbalanced input voltages. The proposed control predicts the behavior of the load current and the instantaneous reactive power for every possible 96 switching states. Subsequently, it selects the optimum switching state which fulfils the objectives of the control without the need of modulators. The cost function has been adequately modified to consider the asymmetrical aspect of the input voltage. Experimental validation using a laboratory prototype was conducted by using FPGA under a wide range of input voltage unbalance. The experimental results show high fidelity load current reference tracking while maintaining relatively low instantaneous reactive power during the transient and steady-state condition. The percentage of reactive power after setting the optimal weighting factor, the average reactive power was found to reduce to approximately 10-20%.

Simple Predictive Current Control of Asymmetrical Six-Phase Induction Motor With Improved Performance

Despite the intensive research work on the application of model predictive control (MPC) for multi-phase machines, there are many challenges still to be handled such as high circulating currents, variable switching frequency, and high computation burden. This paper proposes a simple, yet efficient predictive current control (PCC) for six-phase induction motor (IM) that reduces considerably circulating current, computation cost, and switching frequency. In the proposed method, a group of four candidate voltage vectors (VVs) is formed in each control sample. Unlike similar methods reported in literature, these candidate vectors are generated based on a simple lookup table and the previous optimal VV. The lookup table is designed such that it allows only one commutation in each control sample. The performance of the proposed method is assessed experimentally using a one kW asymmetrical six-phase induction motor (A6PIM). Different performance indices are investigated to compare the proposed method against two different PCC methods in literature at different operating conditions. The experimental results confirm that about 50% reduction in average switching frequency and 15% in current total harmonic distortion at different speed ranges are observed with the proposed method compared to the conventional one.

Multivector Model Predictive Current Control for Paralleled Three-Level T-Type Inverters With Circulating Current Elimination

Paralleled three-level T-type inverters face the unavoidable pitfall of the zero-sequence circulating current (ZSCC). Although the conventional model predictive control (MPC) could eliminate the ZSCC, the control error occurs inevitably since only one vector is adopted in each sampling period, which decreases the current control performance. Thus, simultaneous ZSCC elimination and accurate current tracking become imperative to the inverter using MPC. This article proposed a multi-vector MPC to eliminate the ZSCC and reduce the current distortion without the weighting factor. As the common-mode voltage (CMV) and neutral point (NP) voltage are the main factors that induce the ZSCC, the virtual vector with zero-average CMV is constructed to eliminate the ZSCC. Meanwhile, the small vector is introduced to improve the NP voltage control capability. Additionally, to enhance the current accuracy and reduce the ZSCC ripple, three optimal vectors are adopted to synthesize desired voltage. Moreover, the multi-vector preselection algorithm is proposed to reduce the computational complexity of implementation process. Finally, the effectiveness of the proposed method is verified with comprehensive simulation and experimental results.

Line-Constrained-Based Explicit Model Predictive Current Control for IPMSMs Drives

This paper proposes an improved line-constrained EMPC method for interior permanent magnet synchronous motors (IPMSMs) by utilizing the constraint processing capability of EMPC. Despite the substantial advantages that EMPC provides for motor drives, its memory occupancy and complex weight factor calculation make it limited in tackling only small-scale issues. To address these challenges, the improved method incorporates a constraint variable that converts the area constraints of the conventional EMPC strategy into line constraints. The findings demonstrate that the line-constrained EMPC minimizes the number of piecewise affine (PWA) regions to fewer than ten, which is substantially fewer than those of the conventional method, hence significantly decreasing the algorithm's memory occupancy rate. Moreover, only one weight factor in the cost function exists in the proposed approach, which solves the weight factor calculation problem inherent in the EMPC method. This method's superiority is demonstrated through simulations and experiments.

An Improved Model-Free Active Disturbance Rejection Deadbeat Predictive Current Control Method of PMSM Based on Data-Driven

This article proposes an improved model-free active disturbance rejection deadbeat predictive current control(ADRDPCC) method for permanent magnet synchronous motor (PMSM) used in more electric aircraft (MEA) based on the data driven method, which is used to solve the parameter mismatch problem of deadbeat predictive current control (DPCC) and improve the performance of PMSM control system. DPCC model applied to the current loop of the MEA motor is established as the main control strategy of the system. The principle of active disturbance rejection control is combined with DPCC, and the ADRDPCC structure is formed to optimize the control strategy. A specific extended state observer (ESO) of ADRDPCC is designed to track the internal disturbance caused by parameter mismatch and the external disturbance in real time. DPCC is used as the control law of the ADRDPCC structure to predict the current and output the reference voltage based on the observation of ESO. A deep reinforcement learning model based on ADRDPCC is designed and trained based on the data-driven method. The trained model can compensate and optimize ADRDPCC based on the disturbance observed by ESO and the observed control state of PMSM. The simulated and experimental results show the superiority of the proposed method.

Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives

To eliminate the influence of the parameter mismatches and obtain high model quality, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed in this paper and applied to the permanent magnet synchronous motor (PMSM) speed control system. Since the ARMA model group which is a family of mathematical models containing AR, MA and ARMA structures considers operating states within several sampling periods to achieve better model accuracy, the plant is online designed as this type, and its coefficients are estimated according to the sampled data by the normalized least-mean-square (NLMS) algorithm with adaptive normalized step length to achieve improved model quality with reduced calculation burden. Compared with the ultra-local MF-PCC strategy, advantages of better stator current quality and robustness are demonstrated by the experimental results, as well as the reduced calculation burden compared with the recursive least square (RLS) algorithm used to estimate the coefficients.

Model-Free Finite-Set Predictive Current Control With Optimal Cycle Time for a Switched Reluctance Motor

Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly non-linear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the pre-defined model controller.

Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

A Robust DPCC for IPMSM Based on a Full Parameter Identification Method

This paper proposes a robust deadbeat predictive current control (DPCC) method for the interior permanent magnet synchronous motor (IPMSM) based on a full parameter identification method. The DPCC method is innovatively combined with the high frequency (HF) currents injection method, which can observe inductances, flux linkage, and resistance values of the IPMSM online. After the parameter identification results are obtained, the initial crude parameters are replaced by the identification values in the DPCC method to enhance the robustness of the IPMSM control system. Moreover, a novel online accuracy verification method for parameter identification results is proposed to judge the error estimation level. The proposed method extracts the high bandwidth and the high parameter sensitivity characteristic of the DPCC method. The online identification and verification of IPMSM parameters are proceeded subsequently while achieving robust control of the IPMSM with the DPCC method. Finally, the proposed method is experimentally demonstrated with a 1.5kW motor. The experimental results show that the IPMSM with the proposed method can still work well even in the situation of severe initial parameters mismatch.

Continuous-Control-Set Model Predictive Current Control of Asymmetrical Six- Phase Drives Considering System Nonidealities

Finite-control-set model predictive control (FCS-MPC) of multiphase ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -phase, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is assumed to be an odd number for simplicity) drives is challenging because of the large number of actual/virtual voltage vectors and the need for current control in ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -1)/2 sub-spaces (or planes; multi-plane current control). Any sub-optimal design (poor or no current control in some of the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -1)/2 planes) may result in high individual plane current ripples, due to the low reactance. This work therefore investigates continuous-control-set (CCS) MPC for constant switching frequency multiphase motor drives as another alternative. The high-bandwidth CCS-MPC is designed to accurately account for system non-idealities, namely digital control and pulse width modulation delays, inverter dead time, and measurement noise. It will be shown that the CCS-MPC has the advantages of full voltage vector space access, regular switching characteristic, and improved cycle-by-cycle tracking control, while maintaining some of the known advantages of the FCS-MPC, e.g., intuitive cost function design, model-based control, and fast dynamics. The proposed control scheme is benchmarked experimentally against the classical, proportional-integral-based, field-oriented control in conjunction with an asymmetrical six-phase induction motor drive.