Space Vector Modulation of a Voltage fed Inverter Using Artificial Neural Networks

Muthuramalingam, A.; Sivaranjani, D.; Himavathi, S.

doi:10.1109/indcon.2005.1590218

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The input of the network is the phase angle of the reference voltage vector * and the outputs are fifteen turn-on functions g A ( *), g B ( *), g Fig. 3 Complete ANN-SVPWM controller g 21B ( *), g 11C ( *), g 21C ( *), g 11A ( *), g 21A ( *), g 11B ( *), g 21B ( *), g 11C ( *), g 21C ( *) for the three phases A, B, and C. The equations (3,4,5) are used for generating neural network training data. This subnet use tansig neurons in the hidden layers and purelin neurons in the output layer, and it is obtained by training with trainlm function which is implemented by Levenberg-Marquardt algorithm.…”

Section: A Angle Subnetmentioning

confidence: 99%

“…The coefficient 0 , 1 and 2 in three mode respectively are given by equations (4,6,8). These equations has been used for generating neural network training data.…”

Section: B 0 1 and 2 Calculation Subnetmentioning

confidence: 99%

“…A multi-layer feed forward network based SVM proposed in [3] that shows good performance in both regions of modulation. The effect of the increase in the number of layers with the total number of neurons being constant is investigated in [4]. The performance of the inverter is compared for the different architectures of NN based SVM.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Neural Network Based Space Vector PWM for Permanent Magnet Synchronous Motor Drive

Gao

Huang

2007

2007 International Conference on Mechatronics and Automation

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This paper presents a artificial neural network based implementation of space vector pulse width modulation(SVPWM) for voltage-source inverter(VSI) permanent magnet synchronous motor(PMSM) drive. This ANN-SVPWM completely covers the undermodulation and overmodulation modes using method of linear modulation. Different network architectures are investigated keeping the same number of neurons. A optimal neural network is obtained. The vector control system of PMSM with proposed ANN-SVPWM is simulated using MATLAB/Simulink together with the Neural Network Toolbox. The simulation results demonstrate the feasibility of the optimal network presented.

show abstract

Section: A Angle Subnetmentioning

confidence: 99%

“…The coefficient 0 , 1 and 2 in three mode respectively are given by equations (4,6,8). These equations has been used for generating neural network training data.…”

Section: B 0 1 and 2 Calculation Subnetmentioning

confidence: 99%

See 1 more Smart Citation

A Neural Network Based Space Vector PWM for Permanent Magnet Synchronous Motor Drive

Gao

Huang

2007

2007 International Conference on Mechatronics and Automation

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An Artificial Neural Network Based SVPWM Controller for PMSM Drive

Cai¹,

Y²,

Lin³

et al. 2009

2009 International Conference on Computational Intelligence and Software Engineering

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The research of speed sensorless control of PMSM based on MRAS and high frequency signal injection method

Zhao

Liu

Feng

2010

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering

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When PMSM is controlled without speed sensor the model reference adaptive system (MRAS) has not the good estimation accuracy of rotor position in low speed because it relies on parameters of the motor severely. For this problem, this paper takes "algorithms switching control method" at the full speed range, in high speed segment using the MRAS, while in low speed segment using high frequency signal injection method. And through "the velocity error average method" gets switch points of the two control algorithms. Through the simulation and results analysis prove that the motor at full speed range, which is controlled with different control algorithm under different speed segments, the speed estimation accuracy is better and has the ideal control effects.

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Space Vector Modulation of a Voltage fed Inverter Using Artificial Neural Networks

Cited by 8 publications

References 7 publications

A Neural Network Based Space Vector PWM for Permanent Magnet Synchronous Motor Drive

A Neural Network Based Space Vector PWM for Permanent Magnet Synchronous Motor Drive

An Artificial Neural Network Based SVPWM Controller for PMSM Drive

The research of speed sensorless control of PMSM based on MRAS and high frequency signal injection method

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