Accurate pressure prediction of a servo-valve controlled hydraulic system

The main goal of this paper is to predict the chamber pressures in hydraulic cylinder of a servo-valve controlled hydraulic system accurately using advanced modeling tools like artificial neural networks. After showing that the black-box modeling approaches are not sufficient for long-term prediction of pressures, a structured neural network model is proposed to capture the pressure dynamics of this inherently nonlinear system. The paper shows that the proposed network model could be easily trained to predict the pressure dynamics of an experimental hydraulic test setup provided that the training session is initiated with the weights of the developed model.

► Models to predict pressure in cylinder chambers of a hydraulic system are devised.
► Black-box models are found to be insufficient for long-term prediction of pressures.
► A structured neural network is proposed to predict pressure dynamics accurately.
► Proposed network could be easily adapted to model other similar hydraulic systems.