Adaptive speed identification for vector control of induction motors without rotational transducers

Schauder, C.

doi:10.1109/ias.1989.96696

Cited by 321 publications

(252 citation statements)

References 2 publications

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“…In recent years, several sensorless control schemes have been proposed and are currently used in various fields of electric drive. They usually consist in an adaptive system with a model of reference (MRAS) [2][3] or in Kalman filter [4][5]. Other works use a state observer with an adaptation loop [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Speed Observer using Artificial Neural Network for Sensorless Vector Control of Induction Motor Drive

2012

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Original scientific paperThis paper presents an adaptive speed observer for an induction motor using an artificial neural network with a direct field-oriented control drive. The speed and rotor flux are estimated with the only assumption that from stator voltages and currents are measurable. The estimation algorithm uses a state observer combined with an intelligent adaptive mechanism based on a recurrent neural network (RNN) to estimate rotor speed. The stator and rotor resistances are estimated by a simple Proportional-Integrator (PI) controller, which reduces sensitivity to variations, due essentially to the influence of temperature. The proposed sensorless control scheme is tested for various operating conditions of the induction motor drive. Experimental results demonstrate a good robustness against load torque disturbances, the estimated fluxes and rotor speed converge to their true values, which guarantees that a precise trajectory tracking with the prescribed dynamics.Key words: Field oriented control, Induction motor drive, Recurrent neural network, Sensorless drive Adaptivni estimator brzine za bezsenzorsko vektorsko upravljanje asinkronim motorom zasnovan na umjetnoj neuronskoj mreži. Ovajčlanak opisuje adaptivni estimator brzine temeljen na umjetnoj neuronskoj mreži, koji se primijenjuje na asinkroni motor pogonjen izravnim vektorskim upravljanjem. Brzina i magnetski tok rotora estimiraju se uz pretpostavku dostupnosti mjerenja napona i struja statora. Algoritam koristi estimator stanja u kombinaciji s inteligentnim adaptivnim mehanizmom temeljenim na povratnoj neuronskoj mreži (RNN) kako bi se estimirala brzina rotora. Otpori statora i rotora estimiraju se jednostavnim Proporcionalno-Integralnim (PI) regulatorom,čime se smanjuje osjetljivost na varijacije uzrokovane utjecajem temperature. Predložena bezsenzorska upravljačka shema testirana je za različite radne uvjete asinkronog motora. Eksperimentalni rezultati pokazuju visoki stupanj robusnosti s obzirom na poremećaj momenta tereta, a estimirani tokovi i brzina rotora konvergiraju prema stvarnim vrijednostima što garantira precizno praćenje trajektorija uz zahtijevanu dinamiku.Ključne riječi: vektorsko upravljanje, asinkroni motor, povratna neuronska mreža, bezsenzorsko upravljanje

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Section: Introductionmentioning

confidence: 99%

Adaptive Speed Observer using Artificial Neural Network for Sensorless Vector Control of Induction Motor Drive

2012

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“…The explanation is that as it's noticed in [36], the system must be stable during the adaptation. In order to meet this term, the speed is considered constant during the adaptation procedure which is an extra supposition.…”

Section: Neural Network As the Adaptation Mechanismmentioning

confidence: 99%

“…One of the most commonly used techniques for speed sensorless control is known as MRAS (Model Reference Adaptive System) which is often approached to rotor flux based technique [36][37], though it's possible to use other parameters such as Electromotive Force [38] which is used in this work, as it will later be explained in this chapter.…”

Section: Model Reference Adaptive Systemsmentioning

confidence: 99%

Modèle temps réel d'un estimateur de vitesse à base de réseaux de neurones pour une machine asynchrome /

Haghgoeian¹

2005

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“…The MRAS method is based on the comparison between the outputs of two estimates [4]. The output errors are then used to drive a suitable adaptation mechanism that generates the estimated speed [5][6][7][8]. The MRAS rotor flux is the most popular MRAS strategy, the main problem of model-based sensor drive are related low-speed range.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Kalman Filter for Sensorless Vector Control of Induction Motor

Ghlib¹,

Messlem²,

Gouichiche³

et al. 2017

IJPEDS

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This paper presents a novel neural adaptive Kalman filter for speed sensorless field oriented vector control of induction motor. The adaptive observer proposed here is based on MRAS (model reference adaptive system) technique, where the linear Kalman filter calculate the stationary components of stator current and the rotor flux and the rotor speed is calculated with an adaptive mechanism. Moreover, to improve the performance of the PI classical controller under different conditions, a novel adaptation scheme based on ADALINE (ADAptive LInear NEuron) neural network is used. It offers a solution to the PI parameters to stabilize automatically about their optimum values and speed estimation to converge quicker to the real. The proposed adaptive Kalman filter represents a good comprise between estimation accuracy and computationally intensive. The simulation results showed the robustness, efficiency, and superiority of the proposed scheme compared to the classical method even in low speed region.

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Adaptive speed identification for vector control of induction motors without rotational transducers

Cited by 321 publications

References 2 publications

Adaptive Speed Observer using Artificial Neural Network for Sensorless Vector Control of Induction Motor Drive

Adaptive Speed Observer using Artificial Neural Network for Sensorless Vector Control of Induction Motor Drive

Modèle temps réel d'un estimateur de vitesse à base de réseaux de neurones pour une machine asynchrome /

Neural Adaptive Kalman Filter for Sensorless Vector Control of Induction Motor

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