Field-oriented control based on adaptive neuro-fuzzy inference system for PMSM dedicated to electric vehicle

Djelamda, Imene; Bochareb, Ilhem

doi:10.11591/eei.v11i4.3818

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The latter provides a fuzzy logic process founded on rules generated during the model's training process. The training examples are employed to estimate the variables of the FIS membership function [20,21]. The ANN is used by ANFIS due to its capacity to categorize and recognize patterns.…”

Section: Development Of the Anfis Systemmentioning

confidence: 99%

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Chakhrit,

Djelamda,

Bougofa

et al. 2024

Preprint

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Failure mode and effects analysis (FMEA) is a systematic and structured method employed across diverse industries to proactively identify and evaluate potential failure modes. In a traditional FMEA, for all failure modes, three criticality parameters: severity, detection, and frequency, are assessed to evaluate criticality. Nevertheless, it frequently has certain flaws. Therefore, in this work, a fuzzy risk proposed model is used to improve the use of the FMEA methodology. The new model uses a fuzzy inference technique in place of the conventional criticality calculation. Fuzzy logic technique is used where the various factors are given as members of a fuzzy set fuzzified by employing adequate membership functions to evaluate the risk and then ranking failure modes and preferring measures to control the risks of undesired events. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is suggested as a dynamic, intelligently proposed model to improve and validate the results acquired by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. Finally, an automotive parts industry case is presented to show the potential of the suggested model. This analysis offers a different ranking of failure modes and improves the decision-making by providing a “preventive –corrective plan. A comparison with existing approaches is presented to demonstrate the efficiency of the suggested approach.

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Section: Development Of the Anfis Systemmentioning

confidence: 99%

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Chakhrit,

Djelamda,

Bougofa

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides that, they make switching frequency of power converters not constant, even though it was fixed. Integrating pulse-width modulation (PWM) into DTC makes switching frequency constant, and provides improved performance of the torque and the current [6]- [17]. Popular PWM methods are sinusoidal PWM (SPWM) [6]- [8] and space vector PWM (SVPWM) [8]- [10], [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

An improvement of direct torque controlled PMSM drive using PWM technique and kalman filter

Dũng

Brandštetter

2023

Bulletin EEI

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The paper describes pulse-width-modulation (PWM) technique and Kalman filter (KF) process to improve performance of direct torque controlled permanent magnet synchronous motor (DTC-PMSM) drive. Performance of DTC methods are strongly affected by high stator current ripple. For lowering the ripple, high switching frequency space vector PWM and KF are utilized in the paper. Mathematical model of PMSM and calculations of important quantities of DTC applied to PMSM drive are presented in the ﬁrst part. The second part shows computation process of space vector PWM and KF. Performance indices are utilized to evaluate the drive structures. Theorectical assumptions are validated via simulations with Gaussian noised stator current measurement.

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“…Control [13]. On the other hand, FOC and DTC strategies are not embedded by default in all the VFD used in industries, especially in small or older ones, depending on the hardware capabilities.…”

mentioning

confidence: 99%

An Intelligent Controller Based on LAMDA for Speed Control of a Three-Phase Inductor Motor

Morales,

Fabara,

Pozo

2023

Emerg Sci J

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Three-phase induction motors are widely used in the industrial field due to their low cost and robustness; therefore, it is essential to continuously develop new proposals that improve their behavior and response in applications where speed control is required. This paper proposes the development of an intelligent controller programmed in a PLC and interconnected with a three-phase induction motor through a VFD. The novel intelligent controller bases its operation on the LAMDA algorithm, which acts as a decision-making system based on the state of the error with respect to the speed reference and its derivative, obtaining a closed-loop controller. In addition, the VFD receives commands from the PLC to operate the motor at a constant voltage-frequency ratio in which flux remains constant. The proposed controller has been validated in two study cases: i) reference changes and ii) rejection of disturbances. The results obtained are promising and show a good performance of the LAMDA controller when compared qualitatively and quantitatively with the controller most commonly used in industrial systems, such as PID, and controllers with similar characteristics, such as fuzzy, based on Mamdani and Takagi-Sugeno inference. Doi: 10.28991/ESJ-2023-07-03-01 Full Text: PDF

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Field-oriented control based on adaptive neuro-fuzzy inference system for PMSM dedicated to electric vehicle

Cited by 5 publications

References 15 publications

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

An improvement of direct torque controlled PMSM drive using PWM technique and kalman filter

An Intelligent Controller Based on LAMDA for Speed Control of a Three-Phase Inductor Motor

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