Model Predictive Flux Control Research Articles

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

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

Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors

In this paper, a low-computational burden model predictive flux control (MPFC) based on discrete space vector modulation (DSVM) and the optimal switching sequence (OSS) is proposed for achieving switching frequency (SF) and computational burden efficiencies in motor drives fed by two-level voltage source inverter. The DSVM is used to extend the prediction candidates of MPFC and greatly improve the performance of the controller. A generalized minimum flux error method independent of the number of virtual vectors is derived to cancel the exhaustive optimization method and lower the execution time of the proposed algorithm. In addition, new over-modulation and OSS schemes are designed to optimize the use of DC-link voltage and mitigate the inverter SF when implementing the optimal control action into switching states. The comparative experimental results show that without significant performance degradation, the proposed strategy provided about 50% SF and 25% execution time reductions compared to the classic MPFC methods.

A Novel Three-Vector-Based Model Predictive Flux Control with Low Computation Complexity for SPMSM

A Novel Three-Vector-Based Model Predictive Flux Control with Low Computation Complexity for SPMSM

A Wide-Speed Range Three-Vector-Based Model Predictive Flux Control with Improved Predictive Model for PMSM Drives

A Wide-Speed Range Three-Vector-Based Model Predictive Flux Control with Improved Predictive Model for PMSM Drives

Fuzzy MPPT operation-based model predictive flux control for linear induction motors

This research focuses on a methodology for connecting the linear induction machine (LIM) using model predictive flux control (MPFC) fed by Kyocera solar cells. Additionally, it incorporates a fuzzy logic regulator (FLR) for maximum power point tracking (MPPT) control. The boost converter's characteristics are regulated using MPPT, allowing it to achieve a voltage gain of up to twice the input voltage. The finite state-model predictive flux control (FS-MPFC) is employed to synthesize the three-phase voltage source converter switching states. The paper proposes an improved FS-MPFC strategy for LIMs, eliminating the cost function's need for a weighting parameter. This is achieved by unifying its terms of traditional control methods and reducing the number of calculation steps. The validity and effectiveness of the introduced system are confirmed by extensive simulation results. The findings revealed that the system can accurately extract maximum power from the Kyocera solar cells and track the preset speed values. In addition, it can enhance performance by minimizing the thrust and primary flux under varying speed and load conditions.

Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints

In various permanent magnet synchronous motor (PMSM) drive applications the torque dynamics are an important performance criterion. Here, time-optimal control (TOC) methods can be utilized to achieve highest control dynamics. Applying state-of-the-art TOC methods leads to unintended overcurrents and torque over- and undershoots during transient operation. To prevent these unintended control characteristics while still achieving TOC performance the time-optimal model predictive control (TO-MPC) is proposed in this work. The TO-MPC contains a reference pre-rotation (RPR) and a continuous control set model predictive flux control (CCS-MPFC). By applying Pontryagin's maximum principle, the TOC solution trajectories for states and inputs of the PMSM are determined neglecting current and torque limits. With the TOC solution, a flux linkage reference for the CCS-MPFC is calculated that corresponds to a pre-rotation of the operating point in the stator-fixed coordinate system. This pre-rotated flux linkage reference is reached in minimum time without overcurrents and torque over- as well as undershoots by incorporating current and torque limits as time-varying softened state constraints into the CCS-MPFC. Simulative and experimental investigations for linearly and nonlinearly magnetized PMSMs in the whole speed and torque range show that, compared to state-of-the-art TOC methods, overcurrents and torque over- as well as undershoots are prevented by the proposed TO-MPC.

A Low-Complexity Modulated Model Predictive Torque and Flux Control Strategy for PMSM Drives Without Weighting Factor

This article proposes a modulated model predictive torque and flux control (M2PTFC) method with low complexity for a two-level voltage source inverter (2L-VSI)-fed permanent magnet synchronous motor (PMSM). The proposed strategy aims to reduce the computation burden and simplify the control implementation of the conventional M2PTFC scheme by reducing the number of candidate voltage vectors at every control sample to the minimum (i.e., only two candidates) and eliminating the weighting factor of the cost function and its corresponding tuning procedures. For these purposes, the proposed method devotes different control objectives to the duty modulation and the cost function evaluation processes while executing them sequentially. First, aiming at torque ripple reduction, a duty modulation strategy is proposed based on the analysis of torque deviations produced by different voltage vectors, which ensures proper selection of the two candidates and restricts active voltage vectors (AVVs) that cause high deviations. Then, these candidates are evaluated based on a cost function assigned to achieve the stator flux control objective. The effectiveness of the proposed control scheme is verified through comparative assessment with the conventional M2PTFC and two existing simplified methods by means of simulation and experimental results.

Model Predictive Torque Control for Permanent- Magnet Synchronous Motors Using a Stator- Fixed Harmonic Flux Reference Generator in the Entire Modulation Range

To increase torque and power conversion of permanent-magnet synchronous motors (PMSMs) at high-speed operation, the dc-link voltage of the inverter must be fully utilized during steady-state and transient operations. This article proposes the incorporation of a harmonic reference generator (HRG) with a pulse clipping (PC) scheme into a model predictive control framework to achieve highest power output. The HRG calculates flux references in the stator-fixed coordinate system for an underlying continuous-control-set model-predictive flux control (CCS-MPFC). These reference trajectories contain the harmonic flux component induced by the voltage hexagon in the overmodulation range and specific switching states (e.g., six-step operation) of the inverter are ensured by the proposed PC scheme. This enables a seamless transition to the overmodulation range including six-step operation and increases the drive's power conversion to its maximum extent. Since the flux differential equation in the stator-fixed coordinate system, used as motor model for the HRG and CCS-MPFC, is able to represent PMSMs with linear and nonlinear magnetization, the proposed approach is well suited for the control of PMSMs with nonlinear magnetization. Extensive transient and steady-state experimental investigations on a highly utilized PMSM in the entire modulation and speed range prove the performance of the proposed approach.

Virtual Three-Level Model Predictive Flux Control With Reduced Computational Burden and Switching Frequency for Induction Motors

Model predictive flux control (MPFC) is an alternative control solution to the classic model predictive torque control (MPTC) for driving induction motors (IMs) without the complexity of tuning the weighting factors. However, when applied in motor drives fed by a two-level converter, it results in higher flux and torque ripples. To improve its performance, this article proposes a virtual three-level MPFC (V3-MPFC) method. The virtual vector method increases the total number of voltage vectors crucial for improving the closed-loop performances of the model predictive control. To reduce the computational burden, direct torque control and novel deadbeat (DB) control strategies are proposed. Compared with existing methods, the proposed DB-based method reduces V3-MPFC to a suboptimization problem and still ensures that the suboptimal solution is equivalent to the global optimal solution provided by V3-MPFC. In addition, the redundant states are exploited to lower the switching frequency (SF) without extending the cost function. The simulation and experiment results show that, compared with existing methods, the proposed strategy effectively reduces the flux and torque ripples, and requires a lower SF and execution time.

Modified Primary Flux Linkage for Enhancing the Linear Induction Motor Performance Based on Sliding Mode Control and Model Predictive Flux Control

This paper proposes a modified primary flux linkage and an improved weighting less model predictive control for linear induction motors (LIMs) to enhance the drive system in terms of linear speed response, wide speed range, efficiency, and computation time. Sliding mode controller is presented in this work to get quick response instead of the use of the PI controller. A weighting less model predictive flux control (MPFC) is employed to eliminate the weighting factor and reduce the computation time. Furthermore, the optimum value of the primary flux linkage is calculated to guarantee higher efficiency under the operation of maximum thrust per ampere, loss minimization control and wider speed range in the field weakening region. The FCS-MPFC uses only the primary flux in the cost function independent on the weighting factor. Moreover, simplified calculation process can be executed greatly in the αβ-coordinates without transformation matrix, where the end-effect is fully taken into consideration. In comparison with the PI controller under different conditions, the proposed control method can achieve faster dynamics with lower thrust ripple, computation time, and so on. Comprehensive simulation and experimental results based on one prototype with 3 kW linear induction machine have verified the advantages of the proposed strategy in this work.

Hybrid-vector Model Predictive Flux Control for PMSM Considering Narrow Pulse

Hybrid-vector Model Predictive Flux Control for PMSM Considering Narrow Pulse

Minimization of Capacitor Voltage Difference for Four-Leg Inverter Dual-Parallel IM System

In the situations where multiple motors operate at the same speed, this article designs a dual-parallel induction motor (IM) system supplied by three-phase four-leg inverter for cost reduction and fault tolerance of the normal system. The dc offset and ac fluctuation of capacitor voltages exist in this system due to the motor phases connected to bus capacitors&#x2019; midpoint. It seriously affects voltage utilization and stable operational range. A novel synchronous control scheme with the ability of ac voltage fluctuation minimization (AVFM) and dc voltage offset elimination (DVOE) is proposed. The unified model of dual IMs is used to design the speed regulator, and the model predictive flux control method is combined with space vector modulation to realize torque and flux control of dual IMs. AVFM is realized by controlling the electrical angle difference of dual IMs as <inline-formula><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula>, which is first presented in this scheme. An angle regulator is proposed to control the electrical angle by adjusting stator flux amplitude. DVOE is realized by injecting a compensatory voltage vector, which is simplified in this scheme to avoid the voltage filter or additional coordinate transformation in the conventional method. Experiment results verify the effectiveness of the proposed scheme.

Model predictive flux control of six-phase permanent magnet synchronous motor with novel virtual voltage vectors

Model predictive flux control of six-phase permanent magnet synchronous motor with novel virtual voltage vectors

An Optimal Reduced-Control-Set Model Predictive Flux Control For 3L-NPC Fed Induction Motor Drive

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–</i> This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme.

Performance Analysis of Multi-Level Inverter-Fed Position Sensorless PMSM Drive Using Modified MPTC

In this article, the performance of multilevel inverter-fed position sensorless permanent magnet synchronous motor (PMSM) drive is analyzed using modified model predictive torque control (MPTC) method using two-level and three-level inverters, respectively. The MPTC method helps to control electromagnetic torque directly, which is not possible in model predictive current control, whereas the conventional model predictive torque and flux control method requires weighing factors that are similar to PI controllers used in field-oriented control or direct torque and flux control method. To simplify the control structure, most of the researchers are looking forward to weighing factorless predictive control strategies. The multilevel inverters required complex control algorithms such as space vector modulation. However, the model predictive control methods made it simple by using an embedded control structure. Furthermore, position sensorless control strategies are much needed because speed sensors are much prone to failures. Therefore, in this article, a model reference adaptive system (MRAS)–based speed sensorless approach is used to eliminate the requirement of a position sensor. The proposed multilevel inverter-fed position sensorless PMSM drive using modified MPTC is simulated in MATLAB/SIMULINK environment under different operating conditions such as under no-load, load, and change in speed. The simulation results are validated with the experimental results.

A Novel Power Sharing Strategy Based on Virtual Flux Droop and Model Predictive Control for Islanded Low-Voltage AC Microgrids

The droop control scheme based on Q − ω and P − V characteristics is conventionally employed to share the load power among sources in an islanded low-voltage microgrid with resistive line impedances. However, it suffers from poor active power sharing, and is vulnerable to sustained deviations in frequency and voltage. Therefore, accurate power sharing and maintaining the frequency and voltage in the desired ranges are challenging. This paper proposes a novel microgrid control strategy to address these issues. The proposed strategy consists of a virtual flux droop and a model predictive control, in which the virtual flux is the time integral of the voltage. Firstly, the novel virtual flux droop control is proposed to accurately control the power sharing among DGs. Then, the model predictive flux control is employed to generate the appropriate switching signals. The proposed strategy is simple without needing multiple feedback control loops. In addition, pulse width modulation is not required and tuning challenges for PI regulators are avoided. In order to evaluate the effectiveness of the proposed microgrid control strategy, simulation analysis is carried out in Matlab/Simulink software environment. The results show that accurate power sharing is achieved while a good dynamic response is provided. Furthermore, the voltage and frequency deviations are significantly improved.

A Three-Vector-Based Model Predictive Flux Control for PMSM Drives

A Three-Vector-Based Model Predictive Flux Control for PMSM Drives

An Improved Finite-Control-Set Model Predictive Flux Control for Asymmetrical Six-Phase PMSMs With a Novel Duty-Cycle Regulation Strategy

This work presents an improved finite-control-set model predictive flux control (FCS-MPFC) for asymmetrical six-phase permanent magnet synchronous machine (ASPMSM) with a novel duty-cycle regulation strategy. Firstly, virtual vector concept is adopted to suppress harmonic currents without additional cost function and the corresponding weighting factor. Then, 12 virtual vectors with higher voltage utilization are developed. Meanwhile, for the sake of strengthening steady-state performance, a novel duty-cycle regulation strategy incorporating the well-known Lagrange multiplier method is proposed. In this strategy, the reconfigurable set of voltage vectors (SVV), which consists of an active and a null virtual vector, can be determined without second modification, hence highly mitigating additional computation burden. Furthermore, in order to realize the purpose of unified switching frequency, the SVVs are implemented in terms of regular rather than irregular PWM signal pattern. Finally, experimental results prove the effectiveness of the proposed method.

A Robust Predictive Torque and Flux Control for IPM Motor Drives Without a Cost Function

Finite control set model predictive torque and flux control of ac motor drives commonly selects optimum inverter switching states through a cost function with a required weighting factor. The weighting factor should be well-tuned in accordance with the motor specifications and operating points. Additionally, the control performance of the drive is degraded under the parametric uncertainties. In this article, a predictive torque and flux control method is proposed for interior permanent magnet synchronous motor drives without a cost function. As a result, the tuning of the weighting factor is avoided. Besides, an estimation scheme based on a data-driven model is adopted, which enhances the robustness of the drive against parametric uncertainties. Instead of directly using the motor parameters, the input and output data of the control system, i.e., voltage and current, are used for estimating three new coefficients employed in the predictions. The performance of an interior permanent magnet synchronous motor drive under the proposed control method is evaluated and compared with those under the conventional predictive torque control method and a recently developed predictive torque control method without a weighting factor. The results confirm the effectiveness of the proposed control method and its superiority over the other two methods.

Discrete Space Vector Modulation Based Model Predictive Flux Control With Reduced Switching Frequency for IM Drive

This article presents an optimal model predictive flux control (MPFC) for a two-level inverter fed induction motor. Integrating discrete SVM into FCS-MPFC enhances the performance of the IM drive. However, conventional DSVM-MPFC requires to enumerate and evaluate a higher number of virtual vectors in the prediction loop. In this paper, a high-efficient and low complexity voltage selection method is proposed to reduce the number of candidate voltage vectors from 38 to 15 without any suboptimality. Both steady-state and transient performances of the proposed method remain the same as the 38-vector based conventional DSVM-MPFC, producing the same cost-function values in all operating conditions. Furthermore, an online switching frequency reduction technique is proposed to achieve a minimum commutation per inverter vector change within each sampling cycle and between adjacent cycles. By appropriately arranging the sequence of real voltage vectors in each sampling cycle, a lower average switching frequency is achieved. The proposed switching frequency reduction method decreases the switching losses without compromising the performance of DSVM-MPFC as only the applied sequences of the real voltage vectors are optimized. Experimental studies are conducted to verify the effectiveness of the proposed algorithm.