Optimized DTC by genetic speed controller and inverter based neural networks SVM for PMSM

Idrissi, Aziz El Janati El; Zahid, Noureddine; Jedra, Mohamed

doi:10.1109/intech.2012.6457760

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Each string (chromosome) is a possible solution to the problem being optimized, and each bit (or group of bits) represents a value or some variable of the problem (gene) well. For the implementation of the GAs, we used tournament selection, arithmetic crossover, and mutation [19,20,26].…”

Section: Genetic Algorithm Controllermentioning

confidence: 99%

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

BELAL,

FLITTI,

ZEGAI

2024

RRST-EE

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Thanks to the positive characteristics of the double stator machine (DSIM), its high reliability, and reduced rotor torque ripples, it has become among the most important multiphase machines included in industrial applications. This article aims to apply the two techniques of artificial intelligence represented by the adaptive neuro-fuzzy inference systems (ANFIS) and the genetic algorithm (GA) for direct torque control (DTC) of the DSIM to improve the performance of the machine. The ability to learn and the parallelism of operation characteristics made exploiting the GA to control the machine possible instead of using the proportional integral controller (PI). Fixed switching frequency obtained, given with the vector selection table and hysteresis, allowed the inclusion of the ANFIS technique in the DTC strategy. Two-level inverters are included to feed the DSIM. Several results prove that the two techniques applied, ANFIS and GA, improve the quality of the electromagnetic torque and flux and the dynamic responses of the DSIM.

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Section: Genetic Algorithm Controllermentioning

confidence: 99%

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

BELAL,

FLITTI,

ZEGAI

2024

RRST-EE

View full text Add to dashboard Cite

show abstract

“…physical laws, the three-phase mathematical model of PMSM can be expressed easily in three-phase (abc) frame [5][6][7][8][9][10][11][12][13][14][15][16][17]. Then, by developing this coupled three-phase mathematical model of PMSM, the (dq) axis of current, voltage and flux can be also obtained from Park and Concordia transformations [33]. So, according to the above steps the electromechanical behavior of PMSM system can be described by the following first order differential equations [33]:…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators

Idrissi¹,

Beniysa²,

Bouajaj³

et al. 2021

JESA

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In this paper, stable and adaptive neural network compensators are proposed to control the uncertain permanent magnet synchronous motor (PMSM). Firstly, the overall uncertainties caused by mathematical modelling, parameters variation during operation and external load torque disturbances are modelled. Secondly, a new motion control scheme, where (d-q) current loops are dotted by two on-line tuning neural network compensators (NNCs), is used to compensate these uncertainties. As a result, the speed control loop is processed easily by proportional integral (PI) controller. Stability of the closed-loop system is also designed according to the Lyapunov stability. Compared to classical vector control, the simulations of PMSM system at different speeds including nominal, low and high speed, with and without uncertainties, show the effectiveness of the proposed control scheme.

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Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive

Kamel¹,

Djahbar²,

Barkat

et al. 2018

Lecture Notes in Networks and Systems

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Optimized DTC by genetic speed controller and inverter based neural networks SVM for PMSM

Cited by 5 publications

References 13 publications

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

Intelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators

Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive

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