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  • ISSN[Online] : 2643-9875  ||  ISSN[Print] : 2643-9840

Volume 05 Issue 08 August 2022

Development of Neuroxellar Algorithms for Adaptive Pi Adjustable Speed Control Parameters in Mining
1Maia Kevkhishvili,2Valida Sesadze,3Gela Chikadze,4Marina Shengelia
1,2,4The Professor Georgian Technical University
3The Candidate of Technical Sciences
DOI : https://doi.org/10.47191/ijmra/v5-i8-10

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ABSTRACT:

This article discusses the adaptive Pi regulator based on the basic function of a neural network (RBF) and it is used to control the velocity of a vector directional asynchronous motor. The structure of the control circuit consists of the RBF identifier of the standard Pi controller. The RBF Identifier is used to identify the Jacobian meaning of the asynchronous motor online. The "online" change of neural network parameters is performed by the gradient access method without prior training. Pi controller training is performed by using the "online" identification RBF model. It is advisable to test this controller under different conditions to finally make sure the reliability of the management technology we offer. Research has shown that the proposed controller provides good steadiness and stability of the steering system compared to what a conventional Pi controller provides.

KEYWORDS:

RBF neural network Pi regulator, Pi control algorithm, Pi controller, inverted neural control, Pi regulator, RBF neuro based Pi regulator, asynchronous motor. Nowadays, applied technologies of artificial neural networks are developing with special dynamism in the theory of intellectual computing. The following advantages of neurocellular approaches can be noted:  Acceleration obtained as a result of parallel processing of information;  Resilience to changes in management facility and environmental parameters;  Reliability due to the abundance of system elements;  Ability to use information hidden in the management object.

REFERENCES

1) KusumaGottapu, U. SwantosKiran, U.Srikanth Raju3 P.Nagasai,S.Prasad,P. TejeswaraRao. Design And Analysis Of Artificial Neural Network Based Controler For Speed Control Of induction Motor Using DTC. ISSN : 2248-9622,Vol.4,issue 4( Version 1), April 2014,pp.259-264. C.259.

2) Taifour Ali1, Abdelaziz Y.M. Abbas2, EkramHassaboAbaid Osman3. Control of induction Motor Drive using Artificial Neural Network. SUST Journal of Engineering and Computer Science (JECS), vol. 15,No. 2, 2014.

3) Sesadze Valida, Chikadze Gela, "Fundamentals of Management Engineering" textbook ISBN 978-9941-8-3534-6 Tbilisi 2021. 235 p.

4) Chikadze Gela, Sesadze Valida "Mathematics on the computer" textbook ISBN 978-9941-8-3533-9 Tbilisi 2021 296 p

Volume 05 Issue 08 August 2022

There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.


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