Development of the method for joint operation of neural-network tuners for current and speed

Authors

  • Yuri Eremenko Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516, Russian Federation https://orcid.org/0000-0003-4305-4554
  • Anton Glushchenko Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516, Russian Federation https://orcid.org/0000-0001-7500-336X
  • Vladislav Petrov Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516, Russian Federation https://orcid.org/0000-0002-4844-1329

DOI:

https://doi.org/10.15587/1729-4061.2017.117725

Keywords:

direct current electric drive, neural-network tuner, rolling stand

Abstract

Rolling stands are complex nonlinear objects in metallurgical industry. Their parameters can change their values over time. In order to control rolling stands, direct current electric drives and P- and PI-controllers are used with constant parameters. Such systems include two control circuits. The application of algorithms of linear control leads to the deterioration of transition processes because of a change in the operational mode of the stand. This problem can be resolved by tuning the parameters of linear controllers. Neural-network tuners were previously developed for the circuit controllers of armature current and speed, working in real time without a mathematical model of the control object. The main purpose of present research is to solve a task on their joint operation in real time as well. We designed an algorithm that allows joint work of both tuners, which establishes priorities when calling the tuners. The primary one is tuning a controller of the current circuit, and only in the case that a given tuner was not called over several transitional processes, there is the possibility to call the tuner of the speed circuit. The experiments were conducted using a mathematical model of the main electrical drive of a rolling stand under conditions of change in the parameters of armature winding and mechanical part of the drive.

Control system with two neural-network tuners made it possible to improve energy efficiency of the electric drive by 1.9 % compared with the system without tuning. Such a result was achieved by compensating for a drift in the parameters of electric drive and maintaining the overshoot for speed within the required range. If the overshoot happens to exceed the permissible value, power consumption of the unit increases, which we managed to avoid

Supporting Agencies

  • Исследование проведено при финансовой поддержке прикладных научных исследований Министерством образования и науки Российской Федерации
  • договор № 14.575.21.0133 (RFMEFI57517X0133)

Author Biographies

Yuri Eremenko, Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516

Doctor of Technical Sciences, professor

Department of automation and information technologies 

Anton Glushchenko, Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516

PhD, Associate Professor

Department of automation and information technologies 

Vladislav Petrov, Stary Oskol Technological Institute named after A. A. Ugarov (branch of National University of Science and Technology «MISIS» Makarenko str., 42, Stariy Oskol, Russian Federation, 309516

Postgraduate student

Department of automation and information technologies 

References

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Published

2017-12-08

How to Cite

Eremenko, Y., Glushchenko, A., & Petrov, V. (2017). Development of the method for joint operation of neural-network tuners for current and speed. Eastern-European Journal of Enterprise Technologies, 6(9 (90), 17–21. https://doi.org/10.15587/1729-4061.2017.117725

Issue

Vol. 6 No. 9 (90) (2017): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2017 Yuri Eremenko, Anton Glushchenko, Vladislav Petrov

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