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

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

(1) Laboratory of Intelligent Machines, Lappeenranta–Lahti University of Technology LUT, Finland
(2) Laboratory of Intelligent Machines, Lappeenranta–Lahti University of Technology LUT, Finland
(3) Laboratory of Intelligent Machines, Lappeenranta–Lahti University of Technology LUT, Finland
(*) Corresponding author



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

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