Proceedings Vol. 27/28 (2022)
ENGINEERING MECHANICS 2022
May 9 – 12, 2022, Milovy, Czech Republic
Copyright © 2022 Institute of Theoretical and Applied Mechanics of the Czech Academy of Sciences, Prague
ISBN 978-80-86246-51-2 (electronic)
ISSN 1805-8248 (printed)
ISSN 1805-8256 (electronic)
list of papers scientific commitee
pages 325 - 328, full text
The paper deals with the comparison of control approaches to the height of the bellows air spring. These approaches are PI control, a deep reinforcement learning algorithm, and a combination of both control techniques, the neuro-PI controller. In the case of a neuro-PI controller, it is essentially control by a reinforcement learning algorithm, but its control neural network contains only two weights that are gained in the learning process. The advantage of the neuro-PI controller created in this way is that its functionality can be formally verified in the same way as a classic PI controller. However, such a procedure is only possible when controlling relatively simple systems, such as the described application for controlling an air spring, which is a dynamic system with one degree of freedom. All the above approaches are applied to a mathematical model of an air spring created in the Simulink environment; a deep reinforcement learning algorithm is created in MATLAB.
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All papers were reviewed by members of the scientific committee.