Fuzzy Logic Based DTC-SVM for Speed Control of Five-Phase IPMSM

Mehedi, Fayçal; Djilali, Abdelkadir Belhadj; Yahdou, Adil; Bouyakoub, Ismail

doi:10.1109/irsec48032.2019.9078283

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The VC technique based on the hybrid control of an FP-PMSM has been proposed [36], where the PI controller of the speed of an FP-PMSM has been replaced by an FLC and SMC controller. The DTC based on an SVM using the FL algorithm is proposed to minimize the flux and torque ripple of the FP-PMSM [37]. The authors [35] used a DTC-SVM technique for the FP-PMSM drive, where the PI controllers of the speed are replaced by SMC algorithms and this proposed DTC control improved the performances of systems compared to DTC with PI controllers.…”

Section: Ndtc-nsvpwm Strategy Of the Fp-pmsmmentioning

confidence: 99%

Feedforward Neural Network-DTC of Multi-phase Permanent Magnet Synchronous Motor Using Five-Phase Neural Space Vector Pulse Width Modulation Strategy

Mehedi¹,

Benbouhenni²,

Nezli³

et al. 2021

JESA

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In this work, the direct torque control (DTC) is applied to the five-phase permanent magnet synchronous motor (FP-PMSM). The DTC method based on classical space vector pulse width modulation (SVPWM) is a common solution used to overcome traditional problems; such as stator flux ripple, electromagnetic torque ripple and gives more total harmonic distortion (THD) of the stator current. The actual paper is based on improving the performance of DTC-SVPWM by using the feedforward neural networks (FNNs) instead of the proportional-integral (PI) regulators and hysteresis comparators (HCs) of the conventional SVPWM strategy. This algorithm can solve the traditional PI regulators and HCs problems which are represented in responses dynamic and reduce the torque ripple, flux ripple, and the THD of stator current of FP-PMSM drives. The proposed strategy was tested in different tests with simulation using Matlab software.

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Section: Ndtc-nsvpwm Strategy Of the Fp-pmsmmentioning

confidence: 99%

Feedforward Neural Network-DTC of Multi-phase Permanent Magnet Synchronous Motor Using Five-Phase Neural Space Vector Pulse Width Modulation Strategy

Mehedi¹,

Benbouhenni²,

Nezli³

et al. 2021

JESA

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show abstract

“…In addition, fuzzy controllers and GAs have been employed in IPMSM. A fuzzy logic speed controller has been designed to reduce torque ripples and flux ripples in [12]. An optimization method, based on GA, for optimal trajectories calculation has been proposed [13].…”

Section: Introductionmentioning

confidence: 99%

A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Liu

et al. 2021

Mathematics

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In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

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Fuzzy Logic Based DTC-SVM for Speed Control of Five-Phase IPMSM

Cited by 2 publications

References 17 publications

Feedforward Neural Network-DTC of Multi-phase Permanent Magnet Synchronous Motor Using Five-Phase Neural Space Vector Pulse Width Modulation Strategy

Feedforward Neural Network-DTC of Multi-phase Permanent Magnet Synchronous Motor Using Five-Phase Neural Space Vector Pulse Width Modulation Strategy

A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

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