Intelligent robust controllers for tribotronic conical fluid film bearings


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Abstract

The article presents the results of the development of means for intelligent robust control of the parameters of a tribotronic rotor-support system with a tapered bearing with a removable bush. The proposed controller is implemented on the basis of deep Q-network reinforcement learning (DQN) synthesized on the basis of a numerical model of a rotor support system. The control strategy involved simultaneous control of the shaft position and friction in the lubrication layer. Methods for control synthesis are presented for both a deterministic system and a system with stochastic parameters. In the latter case, a controller synthesis technique is proposed that takes into account uncertainties in the system at the training stage. Testing of the resulting controllers shows the better ability of a controller trained to take into account uncertainties to cope with variable loads, as well as predict possible changes in the system and proactively transfer the system to more advantageous states.

About the authors

Yu. N. Kazakov

Orel State University named after I.S. Turgenev

Author for correspondence.
Email: KazakYurii@yandex.ru
ORCID iD: 0000-0002-9655-4520

Postgraduate Student of the Department of Mechatronics, Mechanics and Robotics

Russian Federation

D. V. Shutin

Orel State University named after I.S. Turgenev

Email: rover.ru@gmail.com
ORCID iD: 0000-0003-0515-7106

Candidate of Science (Engineering), Associate Professor of the Department of Mechatronics, Mechanics and Robotics

Russian Federation

L. A. Savin

Orel State University named after I.S. Turgenev

Email: savin3257@mail.ru
ORCID iD: 0000-0002-0466-0044

Doctor of Science (Engineering), Professor of the Department of Mechatronics, Mechanics and Robotics

Russian Federation

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