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

Julia Malysheva; Ming Li; Heikki Handroos

doi:10.15866/iremos.v13i1.17635

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

Julia Malysheva^(1*), Ming Li⁽²⁾, Heikki Handroos⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iremos.v13i1.17635

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.
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Keywords

Faster Than Real-Time Simulation; Hydraulic Position Servo System; Hydraulic System Model; NARX Neural Network; Recurrent Neural Network

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