Candidate Voltage Vectors Research Articles

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

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

Unidirectional Finite Control Set-Predictive Torque Control of IPMSM Fed by Three-Level NPC Inverter with Simplified Voltage-Vector Lookup Table

This paper proposes a unidirectional finite control set-predictive toque control (UFCS-PTC) method for a three-level neutral-point-clamped (3L-NPC) inverter fed interior permanent magnet synchronous motor (IPMSM). The proposed algorithm can lower the complexity of PTC fed by 3L-NPC by reducing the number of admissible voltage vectors (VVs) effectively. The candidate VVs are restricted within 60° of the voltage space voltage diagram (VSVD), which is the nearest to the flux trajectory for each 60° flux sector. After the segmentation of the VSVD and flux trajectory, the proposed method can keep VVs in one direction during the prediction process, which can result in significant torque/flux reduction. Therefore, the UFCS-PTC can reduce the number of admissible VVs from twenty-seven to six while achieving excellent steady-state performance in terms of reduced flux and torque ripples. Additionally, the proposed method eliminates the need for weighting factor calculation for neutral point voltage associated with a 3L-NPC inverter. The UFCS-PTC of IPMSM also has other features, such as improved balancing capability of the DC-link capacitors’ voltage, small computation time due to the reduced number of admissible voltage vectors considered in the cost function, and easy implementation. The effectiveness of the proposed method is verified through experimental results.

Priority-Based Model Predictive Control Method for Driving Dual Induction Motors Fed by Five-Leg Inverter

This article proposes a priority-based model predictive control (PMPC) method for a five-leg voltage source inverter (FLVSI) that independently drives dual three-phase induction motors (IMs). In a FLVSI operated by a MPC method, selecting an optimal future voltage vector among all 32 candidate switching states causes a high computational burden. However, by using the PMPC, low computation and current ripples can be achieved at the same time. In the PMPC method, two IMs are prioritized, and the priority of both IMs alternates every control period. For this property, the future voltage vector for the high-priority motor (HM) is selected first; that for the low-priority motor (LM) is selected next. In other words, whereas future switching states of both IMs are optimized by one cost function in the conventional MPCs, those of the HM and LM are optimized by their cost functions in PMPC. Therefore, HM and LM have eight and four candidate voltage vectors for cost minimization, respectively, which results in low computation complexity. Furthermore, current ripples are small because the HM is optimized based on all possible candidate voltage vectors, and both IMs are selected as the HM equally. The PMPC method is demonstrated by simulation and experiment results.

Direct Thrust Force Fault-Tolerant Control of Primary Permanent Magnet Linear Motor With Single DC-Link Current Sensor for Metro Application

In order to improve the fault-tolerant control (FTC) ability of the current sensor of the primary permanent magnet linear motor (PPMLM) drive system, the direct thrust force control (DTFC) strategy with single DC-link current sensor is proposed. According to the principle of voltage vector equivalence, the present basic voltage vector is equivalent to two adjacent sub-basic voltage vectors to achieve the equivalence of the present candidate voltage vector. The principle of different phases based on the fact that the DC-link current is equal to or opposite to one of the phase currents is introduced. The output sequence of the two sub-basic voltage vectors is determined by the principle of different phase mode to ensure that the measured currents in the two adjacent periods are any two-phase currents in the three phases, and then the third-phase current is calculated. Finally, the effectiveness and correctness of the proposed DTFC (P-DTFC) is verified by simulation and experiment.

Improved Model Predictive Current Control With Series Structure for PMSM Drives

In order to improve the control performance of conventional finite-control-set model predictive control (FCS-MPC) method and keep the balance between the steady-state performance and switching frequency of the system, this article proposes an improved MPC method based on the conventional MPC method. First, the reference voltage vector of the next control instant is predicted to determine the candidate voltage vectors of the next two control instants. Then, an optimal vector selection way is proposed, in which candidate voltage vectors selected by the reference voltage vector and designed cost function structure in series. Moreover, the selection process of optimal voltage vector from the candidate voltage vectors is analyzed. Finally, the comparison experiments between the conventional FCS-MPC method and the proposed MPC method are conducted. And experimental results show the validity of the proposed control strategy.

Thrust Ripple Suppression for Linear Induction Machines Based on Improved Finite Control Set-Model Predictive Voltage Control

In this article, two kinds of finite control set-model predictive voltage control (FCS-MPVC) methods have been proposed for linear induction machine to eliminate the cumbersome selection of the weighting factor (WF), achieve lower ripples of both thrust and flux, and reduce the heavy calculation steps. The first method, FCS-MPVC I, uses one voltage vector (VV) during the whole control period, and the second method, FCS-MPVC II, uses two VVs. Moreover, the proposed methods use a single cost function without any WF. This function consists of the errors between primary voltage references with their predicted values in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axis. Depending on the distance between the reference VV (RVV), as calculated using the deadbeat control method, and all candidate VVs (CVVs), the first proposed method, FCS-MPVC I, can select the nearest VV to the RVV as the optimal VV (OVV), which can reduce the redundant evaluation work greatly. Meanwhile, the second proposed method, FCS-MPVC II, applies two VVs, active VV and null VV, to adjust the duty ratio effectively. To avoid the iterated evaluation for all CVVs, one simplified search order excluding unsuitable CVVs has been proposed to determine the optimal OVV, which selects only one active VV from the two adjacent active VVs in the sector owning the RVV. Afterward, based on a prototype of 3-kW arc induction machine with a large radius, extensive simulation and experiments have fully demonstrated that the proposed methods can get much lower thrust ripples and less calculation steps effectively, compared with those of conventional finite control set-model predictive thrust control strategy.

Hybrid Data-Model Predictive Control for Enabling Participation of Renewables in Regulating Reserves Service

A long-horizon hybrid data-model predictive direct power control (HD-MPDPC) scheme is proposed. The hybrid control scheme is for grid-side power electronic converters (PEC) in grid-connected renewable energy sources (RES). Computational burden of conventional MPDPC is alleviated by reducing the number of candidate voltage vectors to be examined in the cost function. This is achieved by employing data-driven forecast of RES output power to identify and eliminate the voltage vectors that do not counteract active and reactive power errors appropriately. RES power is forecasted using recurrent neural networks. The method for eliminating voltage vector candidates is inspired from direct power control, where output of active and reactive power hysteresis controllers along with the sector in which grid voltage vector lies are used to determine the switching states of the grid-side PEC. Thanks to reduced computational complexity, the hybrid predictive control scheme dispatches RES power more reliably over long horizons. This in turn enables RES as a regulating reserves service provider in power systems. Hardware-in-the-loop studies of a grid-connected wave energy conversion system verify reduced computational complexity of HD-MPDPC and its effectiveness in controlling RES output power over long horizons.

An Efficient Model Predictive Torque Control for Induction Motors With Flexible Duty Ratio Optimization

Efforts to improve the drive performance of classical model predictive torque control (MPTC) for induction motors (IM) have continued for decades. Existing methods often suffer from intensive computations, poor robustness, and limited quality. To resolve these problems, this article proposes an efficient MPTC for IM with flexible duty ratio optimization. In the proposed strategy, candidate voltage vectors are restricted to be in a subset of an area enclosed by voltage limit hexagon, where all the possible two-vector combinations and part of three-vector combinations are included. To quickly determine the optimal input voltage, a tailored duty ratio optimization algorithm based on voltage error minimization is designed. Experiments clearly indicate that the proposed strategy can effectively improve the steady performance with respect to classic MPTC and some other typical methods. More importantly, the proposed strategy can be conveniently extended by increasing the number of three-vector combinations to achieve steady performance close to space vector modulation-based direct torque control (SVM-DTC). It is inspiring to see faster dynamic response together with less torque ripple than SVM-DTC is provided during a large transient due to the rigorously enhanced optimality of input voltage. Finally, the robustness of the proposed scheme is experimentally validated.

Noninteger Lexicographic-Optimization-Based Sequential Model-Predictive Fault-Tolerant Control of T-Type Shunt Active Power Filter

Fault-tolerant control (FTC) strategies have been proposed to improve the reliability of power electronic systems. However, the FTC of the three-level T-type converter based shunt active power filter (SAPF) remains limited by redundant devices. To address this problem, an FTC strategy called sequential model predictive tolerant control (SMPTC) is combined with a nonintegral lexicographic optimization (LO) algorithm for solving multiobjective optimization problems. First, the power circuit, space voltage vector diagram, harmonic extraction, and prediction models of the SAPF system are introduced. Second, based on power-switch fault analysis, an adaptive dc bus voltage regulation method is proposed, which ensures that the candidate voltage vectors are fully utilized in the fault state. Third, an LO-SMPTC strategy is proposed by designing a sequential predictive controller that considers the neutral point (NP) voltage and the tracking of the SAPF output current. Compared with the traditional model predictive control, the proposed method not only achieves NP voltage balance and excellent harmonic compensation but also eliminates the selection of weighting factors, thereby improving the control flexibility and time efficiency. Finally, the effectiveness of the LO-SMPTC on the dc bus voltage, NP voltage oscillations, grid current power quality, and pole voltage under different scenarios is demonstrated through experiments.

Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification

Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification.

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.

Weighting Factor-Less Sequential Predictive Control of LC-Filtered Voltage Source Inverters

To eliminate the weighting factor tuning effort of typical finite-set model predictive control (FS-MPC), this paper proposes a weighting factor-less sequential model predictive control (SMPC) scheme for LC-filtered voltage source inverters. Two independent cost functions for minimizing capacitor-voltage and inductor-current tracking errors are deployed in a cascaded structure, eliminating the weighting factor. First, the optimal cascade order of the cost function is selected by the internal relationship of two control variables. Then, a graphical method is proposed to determine the optimal number of candidate voltage vectors selected from the first cost function. Moreover, to realize the strict current limitation, the current-constraint term is proved to be included in the voltage-related cost function. Another attractive feature of the proposed SMPC is that a smoother inductor-current starting response can be obtained compared to typical FS-MPC. Simulation and experimental results verify the feasibility of the presented approach.

Hybrid Decision Based on DNN and DTC for Model Predictive Torque Control of PMSM

To address the issue of poor real-time performance caused by the heavy computational burden of the finite control set model predictive torque control (MPTC) of a permanent magnet synchronous motor (PMSM), a data-driven control method using a deep neural network (DNN) is proposed in this paper. The DNN can learn the MPTC’s selective laws from its operation data by training offline and then substitute them for voltage vector selection online. Aiming to address the data-driven runaway problems caused by the asymmetry between the dynamic and static training data, a hybrid decision control strategy based on DNN and DTC (direct torque control) is further proposed, which can realize four-quadrant operation with a control effect basically equivalent to MPTC. The proposed strategy has great application potential for use in multi-level inverter and matrix converter driving with multiple candidate voltage vectors or multi-step prediction.

Improvement of Steady State Performance for Rotating Vector-Based Direct Torque Control

Matrix converter (MC) rotating vector (RV) has the natural advantage for common-mode voltage (CMV) minimization. However, the application of RV in direct torque control (DTC) strategy for motor drive system is limited, mainly due to the extensive torque ripple and current distortion. To improve the steady state torque and current performance of the traditional RV-based DTC, a novel RV- based DTC method is proposed in this paper. According to the analysis of the angle offset between stator flux and the candidate voltage vector, the vector plane is divided into 12 sectors, and virtual vector synthesized by two adjacent rotating vectors is employed to fill in the blank sectors. Based on this, a 12 sector- switching table combined with a novel mapping table of rotating vectors are established to fulfill the proposed RV-DTC method. Experiments are carried out to verify the proposed method. The results show that compared with the traditional RV-DTC, steady state torque and current performances are evidently improved with the proposed RV-DTC at approximate switching frequency, while zero CMV is reserved.

Candidate Modulation Patterns Solution for Five-Phase PMSM Drive System

Model predictive control (MPC) schemes applied in multiphase permanent magnet synchronous motors (PMSMs) should provide the precise torque output and suppress the harmonic currents at the same time. Then, the trade-off between precise torque output and harmonic currents suppression is inevitable in the finite control set MPC (FCS-MPC). To provide an alternative to this problem, this article presents a new extension strategy of FCS-MPC applied in multiphase PMSMs. The novelty of this article includes two parts. First, the control sets are extended from the candidate voltage vectors to the candidate modulation patterns. These modulation patterns consist of switching signals with specific rules. The second one is the new cost function which realizes the harmonic current suppression with the modulation patterns selection. Two groups of modulation patterns are developed to assess the performance of this structure. The first group is called “semi-controlled modulation pattern” which could provide the precise voltage vectors in the <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> subspace. The second group is called “fundamental torque-controlled modulation pattern” which could further suppress the harmonic currents. These two groups of modulation patterns could realize the required performance with a much lower switching frequency (SW). Moreover, the parameter mismatches in the harmonic subspace caused by the uncertainties of the system and errors in the parameter values are also compensated in the proposed MPC. Finally, the comparative experiments show the performance differences between two modulation patterns and those existing controllers.

An extended-horizon model predictive torque control with computationally efficient implementation for PMSM drives

In this paper, an extended-horizon predictive torque control (PTC) method is proposed with computationally efficient implementation. Predictive control with extended horizon brings important advantages compared to single-horizon methods, such as better steady-state performance, reduced current THD, and lower ripples in torque and stator flux. However, the computational burden is a serious obstacle because the required calculations rise exponentially with the extension of the prediction horizon. This is due to a high number of candidate voltage vectors and also predicting the values of several machine variables for these vectors. Different approaches are employed in this work to make the control method computationally tractable. A voltage vector reduction technique is utilised that significantly decreases the total number of enumerated vectors. Moreover, the stator current prediction is eliminated in the proposed method, based on the inherent features of the PMSM. The proposed method is experimentally implemented and its good performance and advantages are verified.

Direct Thrust Force Control of Half-Open Winding Primary Permanent Magnet Linear Motor

In this paper, a direct thrust force control (DTFC) strategy is proposed for the half-open winding primary permanent magnet linear motor (PPMLM) drive system in order to improve the dynamic response and performance of flux weakening control. The independent voltage active vector (IVAV) is selected as the candidate voltage vector to suppresses the zero sequence current in the proposed DTFC (P-DTFC). Meanwhile, a new switch table is designed to determine the control vector while the complexity of control method is greatly simplified in P-DTFC. The simulation and experimental results verify that P-DTFC maintains better steady-state and dynamic-state performance with lower complexity compared with traditional DTFC (T-DTFC).

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.

Two-Stage Series Model Predictive Torque Control for PMSM Drives

A two-stage series model predictive torque control for permanent magnet synchronous motor drives is proposed in this article. First, the control of torque and flux linkage is converted to double-torque control (i.e., the control of active torque and reactive torque) based on the instantaneous power theory to eliminate the weighting factor in conventional model predictive torque control. Then, the double-torque prediction trajectory and double-torque reference are extrapolated to two control periods. The concept of two-stage series predictive control is proposed in this article, in which the cost functions of two prediction instants are designed to form series control structure. In the proposed method, the first-stage prediction is used to select candidate voltage vectors and the second-stage prediction is used to evaluate the optimal voltage vector. Since the cost function of two control periods in the future is considered when selecting the voltage vectors, the possibility that the same voltage vector is applied to adjacent control periods increases, thereby reducing the switching frequency of the system. Finally, the effectiveness of the proposed method is verified by experiments.

Weighting-Factorless Predictive Torque Control Scheme for Dual Inverter fed Open-End-Winding PMSM With Single DC Source

The conventional predictive torque control (PTC) of open-end-winding permanent magnet synchronous motor drive with single dc source deals with the problems of complex weighting factor tuning, higher ripples in zero sequence current (ZSC) and increased flux and torque ripples. To mitigate the above drawbacks, an effective weighting-factorless PTC scheme is proposed. Initially, the ZSC objective of cost function is evaluated with three types of zero sequence voltages (ZSV) of the system that are available. The magnitude of ZSV corresponding to the minimized ZSC error is selected. Six or seven candidate voltage vectors are arranged in accordance with optimized ZSV. As the ZSC imposes high impact on torque and current performance, the selected candidate voltage vectors can be used to meet the torque and flux objective. To further eliminate the flux weighting-factor, an improved incremental merit-based scheme is proposed in this article. The weighting-factors tuning is avoided in the proposed PTC method which results in reduced ZSC and improved current performance in addition to the reduced ripples in flux and torque as well as lower computational burden. The effectiveness of the proposed PTC method is experimentally verified and compared with the existing PTC methods.