A1 Journal article (refereed), original research

Hydraulic System Modeling with Recurrent Neural Network for the Faster Than Real-Time Simulation


Publication Details
Authors: Malysheva Julia, Li Ming, Handroos Heikki
Publication year: 2020
Language: English
Related Journal or Series Information: International Review on Modelling and Simulations
Volume number: 13
Issue number: 1
Start page: 16
End page: 25
Number of pages: 10
ISSN: 1974-9821
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

Depending on the task of a decision-support system, the underlying
computer simulation can be carried out in real time or faster than real
time. The required high simulation speed is a major obstacle in
employing the more advanced simulation models. The work addresses the
question of the recurrent neural network (RNN) usage for the faster than
real-time simulation of hydraulic systems. Mathematical models of such
systems are computationally expensive for numerical integration due to
their high non-linearity and numerical stiffness. In this paper, a
mathematical-based simulation model has been created using an
experimentally verified mathematical model of a hydraulic position servo
system (HPS). A RNN of the NARX architecture has been developed,
trained and tested on the training data produced by the
mathematical-based simulation model. A preprocessing technique has been
developed and applied to the training data in order to speed-up the
training and simulation processes. The obtained results for the first
time show that the employment of the RNN together with the developed
preprocessing technique ensures the simulation speed-up of the complex
hydraulic system at the expense of a small accuracy decrease. In the
considered case of the HPS, a simulation speed-up of factor 4.8 has been
obtained.


Last updated on 2020-25-05 at 08:36

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