Predictive Current Control Method Research Articles

Low-Switching-Loss Finite Control Set Model Predictive Current Control for IMs Considering Rotor-Related Inductance Mismatch

This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the low-switching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations.

Predictive Control Method Based on Adjacent Vector Confinement Technique for a Three-Phase AC-DC Matrix Converter with High Efficiency

A model predictive current control method is proposed to reduce switching losses in an AC-DC matrix converter. In the proposed control strategy, several vectors are selected from among all possible switching vectors for a given location of the input current reference. The switching vector that minimizes the cost function is applied to the converter in the next sampling period. The principle of the proposed method involves clamping the selected switches to stop performing the switching operation to minimize the number of switchings in every sampling cycle. The total efficiency of the AC-DC matrix converter under the proposed strategy is 91.2% whereas that of the conventional strategy is 89.7%. In addition, unity-power-factor operation is guaranteed and smooth and sinusoidal waveforms are achieved. Finally, simulation and experimental results are demonstrated to confirm the validity of the proposed control strategy.

Deadbeat Predictive Current Control for Modular Multilevel Converters With Enhanced Steady-State Performance and Stability

Model predictive control (MPC) methods are popularly employed in modular multilevel converters (MMCs) due to their fast dynamic response and multiobjective control capability. However, they present some inherent problems, such as computational complexity, variable switching frequency, poor steady-state performance, and tedious weighting factor selection. This article develops a deadbeat predictive current control method for MMCs. This method can realize the reference tracking of ac current and circulating current in one sampling period without error, and thus provide a fast dynamic response as conventional MPC methods. Besides, switching state or voltage level evaluation, cost function calculation as well as weighting factor selection are not required. Therefore, it has a very low calculation burden, which is independent of the number of submodules (SMs). Since a modulation stage is utilized, a fixed switching frequency and consequently a satisfactory steady-state performance are obtained. The effects of time delay, parameter mismatch, and SM capacitor voltage ripple on the control algorithm are discussed. Also, the corresponding improvement measures are provided to further enhance the steady-state performance and system stability of MMCs. The effectiveness and performance of the developed control algorithm are verified by experimental results.

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.

Accurate FCS Model Predictive Current Control Technique for Surface-Mounted PMSMs at Low Control Frequency

In order to enhance the control performance of the surface-mounted permanent magnet synchronous machines (PMSM) working under the low frequency situations, this letter innovatively proposes an accurate finite control set (FCS) model predictive current control (MPCC) method. First, a novel predicting plant model in the continuous-time domain based on the numerical solutions of the PMSM state-space model (differential equations) is developed. Without using the linear discretization implementation, the influence of the low control frequency can be eliminated completely. Besides, a brand-new calculation delay compensation method based on delay time prediction and current precompensation is designed for the proposed FCS–MPCC strategy. Finally, experiments are conducted on a PMSM test bench with the control frequencies of 2 and 1 kHz to comprehensively verify the proposed algorithms.

Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch

In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper proposes a Lundberg perturbation observer that is seldom used in the FCS model predictive control situations to remove the adverse effects caused by resistance and inductance mismatch. Firstly, the system model is established, and the FCS-MPCC mechanism is illustrated. Based on the machine model, the sensitivity of the control algorithm to the parameter mismatch is discussed. Then, the Luenberger perturbation observer that can estimate the general disturbance arising from the parameter uncertainties is developed, and the stability of the observer is analyzed by using the discrete pole assignment technique. Finally, the proposed disturbance observer is incorporated into the FCS-MPCC prediction plant model for real-time compensation. Both simulation and experiments are conducted on a three-phase SPMSM, verifying that the proposed strategy has marked control performance and strong robustness.

Enhanced Model Predictive Current Control Based on Runge–Kutta Approximation for a Voltage Source Inverter

Abstract This paper proposes a model predictive current control (MPCC) method with duty cycle control based on the Runge–Kutta approximation compared to the Forward Euler Approximation for grid-connected three-phase inverters with output LCL filter. Hence, results of proposed MPCC methods alongside of the conventional MPCC have been investigated to find the best strategy. First, all 7 possible switching states have been checked by the discrete-time system model based on two approximation methods to select a state that minimizes the cost function. However, at this stage only one voltage vector is chosen during one control period, which cannot decrease the current ripples to a minimum value. Hence, for having sufficient performance, the sampling frequency is necessary to be selected high. Then, the idea of duty cycle optimization has been introduced by using two voltage vectors (a non-zero and a zero voltage vector) during one control period. Therefore, the duration of two voltage vectors have been defined according to the principle of current error minimization. Finally, the effectiveness of the proposed MPCC method based on the Runge–Kutta approximation has been verified by MATLAB/Simulink and experimental results exhibit a better steady-state performance with less sampling frequency as compared to the conventional strategy.

Model Predictive Current Control Method with Improved Performances for Three-Phase Voltage Source Inverters

In this paper, the model predictive current control (MPCC) method using two vectors has been proposed to control output currents of three-phase voltage source inverters (VSIs) with small current errors and current ripples. Also, the proposed method can reduce switching losses by applying the vector pre-selection technique to the MPCC for the VSI. The VSI generates seven voltage vectors to control the output currents, but the proposed method uses four available voltage vectors with one switch, which are classified by the vector pre-selection method clamping one leg and conducting the largest output current among the three legs to reduce the switching losses. In the proposed method, selecting two future voltage vectors among the four voltage vectors and dividing them in a future sampling period are determined by an optimization process. The proposed method results in the lower total loss, better total harmonic distortion (THD), and smaller current errors than the conventional method with half the sampling period of the proposed method due to the optimal process. Simulation and experimental results of the three-phase VSIs are presented in order to verify the effectiveness of the proposed method.

Continuous Voltage Vector Model-Free Predictive Current Control of Surface Mounted Permanent Magnet Synchronous Motor

To reduce the current ripples of finite control set model predictive control, this paper proposes a continuous voltage vector model-free predictive current control method for the surface mounted permanent magnet synchronous motor (SMPMSM). In the proposed method, the continuous phase and the amplitude of the voltage vector are optimized in a sequence based on their uncoupling feature, and then the steady-state current fluctuation can be reduced. Only six active voltage vectors are enumerated, and the optimal phase is obtained by establishing the Lagrange interpolation polynomial between the cost function and the phase of voltage vectors. Then, the amplitude of the voltage vector with the optimal phase is optimized based on the principle of minimizing the cost function, and the optimal voltage vector is synthesized by a three-vector method. In addition, the ultra-local model of the SMPMSM drive system from the previous study is used to improve the robustness of the proposed method. The current dynamic and steady-state responses are demonstrated through the simulation and experimental results.

Research on predictive current control method of PMSM for continuous switched boost inverter

In this paper, the continuous switched boost inverter (CSBI) is used to improve permanent magnet synchronous motor (PMSM) control performance through predictive control, aiming at the drawback of the traditional PMSM drive system. Between the inverted circuit and the input DC power, a switched boost circuit is added to form a switched boost inverter circuit to achieve higher voltage gain and eliminate the dead band. Meanwhile, the drive system has higher reliability. Moreover, the paper adopts predictive current control (PCC) to control PMSM, comparing with the traditional field oriented control (FOC), PCC has faster dynamic performance and reduces current ripple. Finally, the simulation and experimental results verify the feasibility of the based on the continuous switched boost inverter of predictive current control for PMSM.

Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement

In order to solve the parameter dependence problem in model predictive control, an improved model predictive current control (MPCC) method based on the incremental model for surface-mounted permanent-magnet synchronous motor drives is proposed in this paper. First, the parameter sensitivity of a conventional MPCC method is analyzed, which indicates that the parameter mismatches would cause prediction current error and inaccurate delay compensation. Therefore, an incremental prediction model is introduced in this paper to eliminate the use of permanent magnetic flux linkage in a prediction model. Among the parameter of the incremental prediction model, only inductance mismatch contributes to the prediction error, since the influence of resistance mismatch on the control performance is very small. Therefore, in order to improve the antiparameter-disturbance capability of the MPCC method, an inductance disturbance controller, which includes the inductance disturbance observer and inductance extraction algorithm, is presented to update accurate inductance information for the whole control system in real time. Finally, simulation and experimental results both show that the proposed method can effectively eliminate the influence of the parameter mismatches on the control performance and reduce the parameter sensitivity of the MPCC method.

Robust Nonlinear Predictive Current Control Techniques for PMSM

This paper proposes a robust nonlinear predictive current control (RNPCC) method for permanent magnet synchronous motor (PMSM) drives, which can optimize the current control loop performance of the PMSM system with model parameter perturbation. First, the disturbance caused by parameter perturbation was considered in the modeling of PMSM. Based on this model, the influence of parameter perturbation on the conventional predictive current control (PCC) was analyzed. The composite integral terminal sliding mode observer (SMO) was then designed to estimate the disturbance caused by the parameter perturbation in real time. Finally, a RNPCC method is developed without relying on the mathematical model of PMSM, which can effectively eliminate the influence of parameter perturbation by injecting the estimated disturbance value. Simulations and experiments verified that the proposed RNPCC method was able to remove the current error caused by the parameter perturbation during steady state operation.

Model Predictive Virtual-Flux Control of Three-Phase Vienna Rectifier Without Voltage Sensors

In this paper, a finite-control-set model predictive virtual-flux control (FCS-MPVFC) for a three-phase Vienna rectifier is developed and implemented to achieve voltage sensorless control, which is very robust to distorted grid-side voltage operation. Firstly, by investigating the relationship between virtual-fluxes aroused from grid-side voltages and line voltages in $d$ - $q$ frame, the control object is directly associated with virtual-flux tracking error minimization. Secondly, the reference line virtual-flux is calculated based on the controllability of active/reactive power associated with the delay compensation and load angle. Thirdly, to enhance the steady-state performance, switching sequence rather than single switching vector is utilized during one sampling period. Furthermore, a redundant vector pre-selection is adopted to balance the neutral-point voltage. The proposed strategy is compared with the traditional finite-control-set model predictive current control (FCS-MPCC) method. Both simulated and experimental results verify the effectiveness of the proposed control algorithm.

Research on Model Predictive Current Control for Induction Machine Based on Immune-Optimized Disturbance Observer

To improve the conventional predictive current control (PCC) performance of an induction machine (IM) system, a robust PCC method based on an immune-optimized disturbance observer (DOB) is proposed in this paper. First, a predictive current controller is established and then the performance of the PCC is analyzed when machine parameters mismatch. Second, the model PCC for an IM based on an immune-optimized DOB is proposed in this paper, and then the performance of disturbance rejection is analyzed. However, the fixed constant time of a traditional DOB will largely limit the performance of a closed-loop system. Therefore, the immune algorithm is introduced in this paper and it is used to optimize the fixed parameters of DOB. Finally, the experimental results show that both smooth dynamic responses at transient-state and high-accuracy performance at steady state can be achieved, and the effectiveness of the proposed algorithm is verified.

Predictive Current Control of Boost Three-Level and T-Type Inverters Cascaded in Wind Power Generation Systems

A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the conventional boost converter, the boost-TL converter has a low inductor current ripple, which reduces the torque ripple of the generator, increases the converter’s capacity, and minimizes switching losses. The boost-TL converter can boost the DC output from the rectifier at low speeds. The principles of the boost-TL converter and the T-type three-level inverter are separately introduced. Based on the cascaded structure of the proposed BTL converter and three-level inverter, a model predictive current control (MPCC) method is adopted, and the optimization of the MPCC is presented. The prediction model is derived, and the simulation and experimental research are carried out. The results show that the algorithm based on the proposed cascaded structure is feasible and superior.

Control Strategy of Active Power Filter Based on Modular Multilevel Converter

To improve the capacity, pressure resistance and the equivalent switching frequency of active power filter (APF), a control strategy of APF based on Modular Multilevel Converter (MMC) is presented. In this Control Strategy, the indirect current control method is used to achieve active current and reactive current decoupling control; Voltage Balance Control Strategy is to stabilize sub-module capacitor voltage, the predictive current control method is used to Track and control of harmonic currents. As a result, the harmonic current is restrained, and power quality is improved. Finally, the simulation model of active power filter controller based on MMC is established in Matlab/Simulink, the simulation proves that the proposed strategy is feasible and correct.

Generalized Proportional Integral Observer-Based Robust Finite Control Set Predictive Current Control for Induction Motor Systems with Time-Varying Disturbances

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

A Robust Double Closed-Loop Control Scheme for PMLSM Drives

In this paper, a novel velocity and current double closed-loop control scheme applied to the permanent magnet linear synchronous motor (PMLSM) drives is proposed, and to suppress various uncertainties and disturbances, the advanced observation technique is introduced. First, as the inner loop, the current loop adopts the predictive current control method, which can increase the current control bandwidth and tracking precision. To improve the dynamic response and robustness of the velocity loop, a super-twisting sliding-mode control strategy is presented. Second, to overcome the parameter mismatch issue in the current loop and suppress different force disturbances in the velocity loop, their respective super-twisting sliding-mode observers are structured, and the corresponding estimated values are injected into the double closed loops by the feedforward method. At last, based on the precise PMLSM testing platform, the experimental studies are carried out to prove the effectiveness and correctness of the proposed scheme.

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 predictive current control method for four-leg three-level converter operating as shunt active power filter and grid connected inverter

Abstract This paper presents model predictive control (MPC) with a finite control states set (FS) for three-level four-leg flying capacitor (FCC) converter. Principles of control method are presented and flying capacitor voltages control method is discussed. Experimental results of converter operating as a shunt active power filter (SAPF) and grid connected inverter (GCI) are shown.