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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2011 - 4

PID Neural Network Based Speed Control of Asynchronous Motor using Programmable Logic Controller

MARABA, V. A. See more information about MARABA, V. A. on SCOPUS See more information about MARABA, V. A. on IEEExplore See more information about MARABA, V. A. on Web of Science, KUZUCUOGLU, A. E. See more information about KUZUCUOGLU, A. E. on SCOPUS See more information about KUZUCUOGLU, A. E. on SCOPUS See more information about KUZUCUOGLU, A. E. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (431 KB) | Citation | Downloads: 2,068 | Views: 5,299

Author keywords
control, neural network, PID, PIDNN, PLC

References keywords
control(12), neural(6), system(5), networks(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 23 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04004
Web of Science Accession Number: 000297764500004
SCOPUS ID: 84856594182

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This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC) was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

References | Cited By  «-- Click to see who has cited this paper

[1] Dandil B., Plant control by aid of artificial neural networks, MSc Thesis, Firat University Institute of Applied Sciences, 1997, (in Turkish).

[2] Shu, H., Pi, Y., "PID neural networks for time-delay systems", Computers and Chemical Engineering, Vol. 24, Issues 2-7, pp 859-862, 2000.

[3] Bekiroglu, N., Ozcira, S., "Observerless Scheme for Sensorless Speed Control of PMSM Using Direct Torque Control Method with LP Filter", Advances in Electrical and Computer Engineering, Vol. 10, Number 3, pp. 78-83, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 5]

[4] Laroussi, K., Zelmat, M., Rouff, M. "Implementation of a Fuzzy Logic System to Tune a PI Controller Applied to an Induction Motor", Advances in Electrical and Computer Engineering, Vol. 9, Number 3, pp. 107-113, 2009.
[CrossRef] [Full Text] [Web of Science Times Cited 9]

[5] Sedighizadeh, M., Rezazadeh, A., "A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System Control", Advances in Electrical and Computer Engineering, Vol. 10, Number 2, pp. 153-159, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 4]

[6] Shu, H., Guo, X., Shu, H., "PID neural networks in multivariable control systems", International Symposium on Intelligent Control, Vancouver, Canada, 2002.

[7] Shu, H., Guo, X., "Decoupling Control of Multivariable Time-Varying Systems Based on PID Neural Network", 5th Asian Control Conference, Melbourne, Australia, 2004.

[8] Shu, H., Pi, Y., "Decoupled Temperature Control System Based on PID Neural Network", ACSE Conference, Cairo Egypt, 2005.

[9] Siemens S7-300 Module Data, A5E00105505-06, 08/2009.

[10] Ronco E, Gawthrop P J, Neural networks for modeling and control, Centre for System and Control Department of Mechanical Engineering University of Glasgow, Technical Report csc97008, 1997.

[11] Lennart Ljung, System Identification: Theory for the user, 2nd edition (Prentice Hall PTR, 1999).

[12] Siemens S7-SCL V5.3 for S7-300/400 Manual, Edition 01/2005.

References Weight

Web of Science® Citations for all references: 18 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 2 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-17 23:13 in 27 seconds.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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