Applying a neural tuner of the PI­controller parameters to control gas heating furnaces

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/0000-0002-6948-9807
  • Andrey Fomin 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/0000-0001-9867-2195

DOI:

https://doi.org/10.15587/1729-4061.2017.117743

Keywords:

PI-controller, perturbation action, neural-network tuner, adaptive control, heating furnaces

Abstract

In order to optimize the quality of transition processes on a heating object of control, it is proposed to apply a neural-network tuner, which changes parameters of the PI-controller in real time. The aim of present study is to determine effectiveness of application of the tuner using a model of the heating furnace, containing a gas supply control circuit and a controlling element of this circuit. Simulation was performed on the model of a gas furnace obtained through recalculation of thermal power from the model of an electric furnace. The study confirms the capability of the proposed adaptive system to effectively execute adaptation of parameters of the controller in the presence of a controlling mechanism whose dynamics may negatively affect the quality of control.

The result of applying the tuner is a decrease in the time of transition process by 25.8 % and a reduction in the total controlling influence by 22.85 %. The presence of the controlling element in this case had no significant effect on the work of a neural-network tuner. The result of research makes it possible to extend the class of objects for which a neural-network tuner can be applied. Previously, the tuner demonstrated its effectiveness only for electric furnaces where influence of the controlling element is minimal. Result of the present study makes it possible to scale up the solution for gas thermal furnaces despite a markedly greater influence of the controlling element

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 

Andrey Fomin, 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 

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Published

2017-12-08

How to Cite

Eremenko, Y., Glushchenko, A., & Fomin, A. (2017). Applying a neural tuner of the PI­controller parameters to control gas heating furnaces. Eastern-European Journal of Enterprise Technologies, 6(2 (90), 32–37. https://doi.org/10.15587/1729-4061.2017.117743

Issue

Vol. 6 No. 2 (90) (2017): Information technology. Industry control systems

Section

Industry control systems

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Copyright (c) 2017 Yuri Eremenko, Anton Glushchenko, Andrey Fomin

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