Artificial neural network-based estimation for rotor-flux model reference adaptive system

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253634" target="_blank" >RIV/61989100:27240/23:10253634 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352146523005124" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352146523005124</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.trpro.2023.11.215" target="_blank" >10.1016/j.trpro.2023.11.215</a>

Result language
angličtina
Original language name
Artificial neural network-based estimation for rotor-flux model reference adaptive system
Original language description
At the start, this paper focuses on the function of a rotor-flux model reference adaptive system (RF-MRAS) and in the following part on the realization and application of artificial neural networks (ANN) in a sensorless induction motor drive. Afterwards, a data collection and usage process for the training of ANN is described. In the final part, experimental results of ANN's ability to estimate rotor flux are presented. According to simulations, ANN estimations are accurate and its application as a part of a control scheme looks promising.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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