Open Access Open Access  Restricted Access Subscription or Fee Access

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.
Copyright © 2020 Praise Worthy Prize - All rights reserved.


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

Full Text:



S. Boschert, R. Rosen, Digital Twin - The Simulation Aspect (Cham, Springer, 2016, pp. 59-74).

A. Mikkola, Using The Simulation Model for Identification of the Fatigue Parameters of Hydraulically Driven Log Crane, Journal of Mechanical Design, Vol. 123(Issue 1): 125–131, 1997.

M. E. Baharudin, A. Rouvinen, P. Korkealaakso, and A. Mikkola, Real-Time Multibody Application for Tree Harvester Truck Simulator, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, Vol. 228(Issue 2), pp. 182–198, 2014.

S. Esqué, A. Raneda, and A. Ellman, Techniques for Studying a Mobile Hydraulic Crane in Virtual Reality, International Journal of Fluid Power, 4(Issue 2): 25–35, 2003.

Y. Zheng, T. Ge, and J. Liu, Kinematics Modelling and Control Simulation for a Logging Harvester in Virtual Environments, Advances in Mechanical Engineering, Vol. 7(Issue 10): 1687814015611329, 2015.

M. M. Pedersen, M. R. Hansen, and M. Ballebye, Developing a Tool Point Control Scheme for a Hydraulic Crane Using Interactive Real-Time Dynamic Simulation, Modeling, Identification and Control, Vol. 31(Issue 4): 133–143, 2010.

F. X. C. Andrade, M. Feucht, A. Haufe, and F. Neukamm, An Incremental Stress State Dependent Damage Model for Ductile Failure Prediction, International Journal of Fracture, Vol. 200(Issue 1): 127–150, 2016.

A. Mikkola, Studies on fatigue damage in a hydraulically driven boom system using virtual prototype simulations, D.Sc. thesis, Lappeenranta University of Technology, Lappeenranta, Finland, 1997.

H. E. Merritt, Hydraulic Control Systems (Wiley, 1967).

J. Dormand, and P. Prince, A Family of Embedded Runge-Kutta Formulae, Journal of Computational and Applied Mathematics, Vol. 6(Issue 1): 19–26, 1980.

S. Esqué, A new approach for numerical simulations of fluid power circuits using Rosenbrock methods, PhD thesis, Tampere University of Technology, Tampere, Finland, 2008.

P. Krus, A. Jansson, and J.-O. Palmberg, Real-time simulation of hydraulic control systems with complex mechanical loads, IFAC Symposium on Computer Aided Design in Control Systems, Vol. 24, pp. 351–357, Swansea, UK, July 1991.

A. J. Flueck, High-fidelity, faster than real-time dynamics simulation, 2014 IEEE PES General Meeting - Conference Exposition, pp. 1–1, 2014.

I. Malysheva, H. Handroos, V. Zhidchenko, and A. Kovartsev, Faster than real-time simulation of a hydraulically actuated log crane, 2018 Global Fluid Power Society PhD Symposium (GFPS), pp. 1–6, 2018.

H. Handroos, and M. Vilenius, Using the Simulation Model for Identification of the Fatigue Parameters of Hydraulically Driven Log Crane, Journal of Mechanical Design, Vol. 113(Issue 3): 232–238, 1991.

R. Åman, and H. Handroos, Comparison of numerical effectiveness of three methods for modelling 2-way flow control valves, The 7th International Conference on Fluid Power Transmission and Control, Beijing World Publishing Corporation, pp. 711–715, 2009.

K. Hornik, Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks, Vol. 4(Issue 2): 251–257, 1991.

O. P. Ogunmolu, X. Gu, S. B. Jiang, and N. R. Gans, Nonlinear systems identification using deep dynamic neural networks, arXiv preprint, arXiv:1610.01439, 2016.

F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, Recurrent Neural Networks for Short-Term Load Forecasting (Springer, 2017).

N. K. Sinha, M. M. Gupta, and D. H. Rao, Dynamic neural networks: an overview, IEEE International Conference on Industrial Technology 2000, Vol. 2, pp. 491–496, 2000.

E. Petlenkov, Neural networks based identification and control of nonlinear systems: A NARX model based approach, Ph.D. thesis, Tallinn University of Technology, Tallinn, Estonia, 2007.

A. Patel, and J. Dunne, NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-State And Variable Temperature Operation, Vehicle System Dynamics, Vol. 40 (Issue 5): 285–328, 2003.

H. T. Siegelmann, B. G. Horne, and C. L. Giles, Computational Capabilities of Recurrent NARX Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 27 (Issue 2): 208–215, 1997.

L. Łacny, Modelling of the Dynamics of a Gyroscope Using Artificial Neural Networks, Journal of Theoretical and Applied Mechanics, Vol. 50 (Issue 1): 85–97, 2012.

G. Schram, M. Verhaegen, and A. Krijgsman, System identification with orthogonal basis functions and neural networks, 13th World Congress of IFAC, Vol. 29 (Issue 1), pp. 4150 – 4155, San Francisco USA, 30 June - 5 July 1996.

J. Liu, H. Wu, H. Handroos, and H. Haario, Parameter Estimation of an Electrohydraulic Servo System Using a Markov Chain Monte Carlo Method, Journal of Dynamic Systems, Measurement, and Control, Vol. 135, p. 011009, 2012.

C. Canudas de Wit, H. Olsson, K. J. Åström, and P. Lischinsky, A New Model for Control of Systems with Friction, IEEE Transactions on Automatic Control, Vol. 40 (Issue 3): 419–425. March 1995.

H. Olsson, Control systems with friction, Ph.D. thesis, Dept. of Automatic Control, Lund Institute of Technology, Lund, Sweden, 1996.

M. Jelali, and A. Kroll, Hydraulic Servo-systems: Modelling, Identification and Control (Springer-Verlag London, 2002).

R. Rojas, Neural Networks: A Systematic Introduction (Springer-Verlag Berlin Heidelberg, 1996).

H. Yu, and B. Wilamowski, Intelligent Systems: Industrial Electronics Handbook, Levenberg-Marquardt Training (CRC Press, 2011).

H. B. Demuth, M. H. Beale, O. De Jess, and M. T. Hagan, Neural Network Design (Martin Hagan, USA, 2014).

N. E. Barabanov, and D. V. Prokhorov, Stability Analysis of Discrete-Time Recurrent Neural Networks, IEEE Transactions on Neural Networks, Vol. 13 (Issue 2): 292–303, March 2002.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize