Field-oriented control of induction motors using neural-network decouplers

Ba-Razzouk, A.; Chériti, A.; Olivier, G.; Sicard, Pierre

doi:10.1109/63.602571

Cited by 67 publications

(28 citation statements)

References 14 publications

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“…Estimation of flux from the measured stator currents and voltages has been discussed in [21][22]. Mapping capability of the neural network with the nonlinear dynamic system was well demonstrated in the literature [23].…”

Section: Introductionmentioning

confidence: 88%

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

KamalBasha¹,

Suryakalavathi²

2013

IJCA

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In speed sensorless controlled induction motor drive, the rotor flux based Model Reference Adaptive System (MRAS) for is more popular. In MRAS, the voltage model equations are used in the reference model, which leads to poor performance of the drive at low speeds. In this paper, a new stator current based MRAS is presented. In this measured stator currents of induction motor are used as a reference model to avoid the use of a pure integrator. Estimated stator currents are used as an adjustable model. The estimation of stator currents requires the information of the rotor flux which can be obtained from the voltage or current model. This encounters instability and dc drift. To overcome these problems, flux observer is proposed based on Neural Network (NN). This offline trained multilayer feed-forward NN flux observer applied to indirect vector controlled speed sensorless induction motor drive has been verified by MATLAB/SIMULINK. General TermsNeural Networks, Power Electronics and Drives et. al.

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Section: Introductionmentioning

confidence: 88%

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

KamalBasha¹,

Suryakalavathi²

2013

IJCA

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“…The commonest type of ANN is the multilayer feedforward neural network which consists of layers; each layer consists of neurons [5,9]. Multilayer feedforward neural networks have shown a great capability to model complex nonlinear dynamic systems [10]. Various attempts to model machine flux from measured quantities such as stator voltages, currents and motor speed have been discussed [9,10].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Multilayer feedforward neural networks have shown a great capability to model complex nonlinear dynamic systems [10]. Various attempts to model machine flux from measured quantities such as stator voltages, currents and motor speed have been discussed [9,10].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The neural network output is compared with the target value and a weight correction via a learning algorithm is performed in such a way to minimize the error between the two values. This is an optimization problem in which the learning algorithm searches for the optimal weights that can represent the solution to the approximation problem [10]. Neural Networks can be trained online or off-line.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…To obtain good estimation accuracy, the inputs to the network are the present and past values of the d-q components of the stator voltage and current in the stationary reference frame. The number of neurons in the hidden layer is chosen by a trial error technique as a compromise between computational complexity, if a larger number is selected, and approximation accuracy, if a smaller number is selected [10]. The output layer consists of two neurons representing the rotor flux components in the stationary reference frame.…”

Section: Rotor Flux Estimation Based On the Current Modelmentioning

confidence: 99%

See 2 more Smart Citations

A neural network based stator current MRAS observer for speed sensorless induction motor drives

Gadoue

Giaouris

Finch

2008

2008 IEEE International Symposium on Industrial Electronics

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Abstract-This paper presents a novel Model Reference Adaptive System (MRAS) speed observer for induction motor drives based on stator currents. The measured currents are used as reference model for the MRAS observer to avoid the use of a pure integrator. A twolayer Neural Network (NN) stator current observer is used as the adaptive model which requires the rotor flux information. This can be obtained from the voltage or current model but instability and dc drift can downgrade the overall observer performance. To overcome these problems another off-line trained multilayer feedforward NN is proposed here as a rotor flux observer. Speed estimation performance of the MRAS scheme using the three different rotor flux observers is studied and compared when applied to an indirect vector control induction motor drive. Promising results have been obtained when using the NN flux observer with less sensitivity to parameter variation and stability in the regenerating mode of operation.

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Position sensorless driving of BLDCM using neural networks

Guo

Sagawa

Ichinokura

2007

Electrical Engineering Japan

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A sensorless driving method of brushless DC motors (BLDCM) using neural networks has been studied in this paper. Considering the nonlinear characteristics and the parameter error of the modeling, neural networks are introduced to estimate the electromotive force (EMF). The results of simulation and experiment using offline trained neural networks show that the proposed method is useful. In addition, the robustness about the parameters is discussed.

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Field-oriented control of induction motors using neural-network decouplers

Cited by 67 publications

References 14 publications

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

A neural network based stator current MRAS observer for speed sensorless induction motor drives

Position sensorless driving of BLDCM using neural networks

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