Discrete Space Vector Modulation-Based Model Predictive Torque Control With No Suboptimization

Osman, Ilham; Xiao, Dan; Alam, Kazi Saiful; Shakib, S M Showybul Islam; Akter, Md. Parvez; Rahman, M.F.

doi:10.1109/tie.2019.2946559

Cited by 47 publications

(22 citation statements)

References 30 publications

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“…This can enlarge the voltage degree of freedom and hence address the abovementioned issues regarding the conventional FS-PTC of the PMSM drives. In the conventional PTC-DSVM, the production of is similar to that reported in [18,22,24,25], i.e., it is synthesized by subdividing into three segments, as follows:…”

Section: Synthesis Of Virtual Vvsmentioning

confidence: 99%

“…However, the complex lookup table for storing VVs resulted in a large burden of memory, and the flux and torque ripples were comparable to those of the conventional FS-PTC. Recently, PTC-DSVM with no suboptimality has been introduced [25]; however, this method requires a number of stages for enumerating VVs with various switching tables.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the VV-based lookup tables utilized in previous methods to reduce computation time cannot be applied owing to the tedious implementation and the high complexity that increases the load of memory. Unlike the conventional method [25], the proposed PTC-DSVM introduces a simplified strategy to increase the system accuracy and reduce the computation time by designing an optimal zone of space vector diagram (SVD) for each flux position depending on torque demand, and hence, eliminating the need for the flux sign to control the flux demand. Moreover, large, and complex VV lookup tables are not required.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Alsofyani¹,

Lee²

2020

Electronics

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The conventional finite set–predictive torque control of permanent magnet synchronous motors (PMSMs) suffers from large flux and torque ripples, as well as high current harmonic distortions. Introducing the discrete space vector modulation (DSVM) into the predictive torque control (PTC-DSVM) can improve its steady-state performance; however, the control complexity is further increased owing to the large voltage–vector lookup table that increases the burden of memory. A simplified PTC-DSVM with 73 synthesized voltage vectors (VVs) is proposed herein, for further improving the steady-state performance of the PMSM drives with a significantly lower complexity and without requiring a VV lookup table. The proposed scheme for reducing the computation burden is designed to select an optimal zone of space vector diagram (SVD) in the utilized DSVM based on the torque demand. Hence, only 10 out of 73 admissible VVs will be initiated online upon the optimal SVD zone selection. Additionally, with the proposed algorithm, no flux error is required to control the flux demand. The proposed PTC-DSVM exhibits high performance features, such as low complexity with less memory utilization, reduced torque and flux ripples, and less redundant VVs in the prediction process. The simulation and experimental results for the 11 kW PMSM drive are presented to prove the effectiveness of the proposed control strategy.

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Section: Synthesis Of Virtual Vvsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Alsofyani¹,

Lee²

2020

Electronics

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“…In the widely used conventional MPCC [27]- [28], the number of switching states generated from the inverter is limited, resulting in a single voltage vector application. In [29]- [30], the discrete space vector modulation (DSVM) is integrated into the MPC as a practical solution to vector constraints. The DSVM takes advantage by increasing the candidate voltage vectors with the synthesis of virtual voltage vectors.…”

Section: Introductionmentioning

confidence: 99%

Triple-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SPMSM Based on Discrete-Space-Vector Modulation

Agustin

Lin

et al. 2021

IEEE Access

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The four-switch three-phase (FSTP) inverters are known for their cost-effective advantages and minimal switching losses. However, such inverter topology's progress is lagging due to control constraints and requirements, including voltage vector limitations and parameter perturbations. To overcome the issue, this paper proposes a triple-voltage-vector model-free predictive current control (TVV-MFPCC) for FSTP inverter-fed surface permanent magnet synchronous motor (SPMSM) drives. The proposed TVV-MFPCC uses the principle of discrete-space-vector modulation (DSVM) to increase the voltage vector selections. Three primary voltage vectors, either the same or distinct, are linearly combined to yield the synthesized voltage vectors. A redundant voltage vector reduction scheme is also introduced to lessen calculations by optimally reducing the candidate voltage vectors to sixteen equivalent hybrid switching modes. To improve prediction accuracy, the TVV-MFPCC performs three different current readings and three current difference calculations in each sampling period. Experiments using a TMS320F28379D microcontroller are conducted to compare the performance of the proposed TVV-MFPCC against conventional MFPCC (C-MFPCC) and validate the scheme.INDEX TERMS Discrete-space-vector modulation, four-switch three-phase inverter, model predictive current control, model-free predictive control, triple-voltage-vector.

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“…At present, the constrained control of induction motor is being developed from the traditional feedback-based control represented by the PI controller to optimize control such as model predictive control (MPC) [11][12][13]. With the improvement of the computing performance of digital processors and the pursuit of higher performance power electronic devices, MPC has attracted more and more attention and research in the field of power electronics [14][15][16][17][18][19][20][21].…”

mentioning

confidence: 99%

Acoustic Noise of Induction Motor With Low-Frequency Model Predictive Control

2020

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This paper studies the acoustic noise of induction motor with low switching frequency model predictive control and compares its noise performance with the traditional pulse width modulation with proportional-integral controllers. First, the relationship among the air-gap magnetic field, the electromagnetic force, and the acoustic noise is modeled to describe the influence of control algorithm on the noise characteristics of motors. As the counterpart with the traditional pulse width modulation control, a finite control set model predictive control strategy with low switching frequency is proposed. The steady-state performance and switching frequency constraint ability of the proposed model predictive control strategy are verified on the experimental platform. Second, the noise spectrums of these two control strategies are sampled and compared in different operating conditions, and the influence of the weighting factor on the noise spectrum is also discussed. It is proved by the experimental results that model predictive control has better noise performance than the traditional sinusoidal pulse width modulation under the same switching frequency and the total harmonic distortion of stator current.

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Discrete Space Vector Modulation-Based Model Predictive Torque Control With No Suboptimization

Cited by 47 publications

References 30 publications

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Predictive Torque Control Based on Discrete Space Vector Modulation of PMSM without Flux Error-Sign and Voltage-Vector Lookup Table

Triple-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SPMSM Based on Discrete-Space-Vector Modulation

Acoustic Noise of Induction Motor With Low-Frequency Model Predictive Control

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