Pressure prediction on a variable-speed pump controlled hydraulic system using structured recurrent neural networks

• 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.

This paper presents a study to predict the pressures in the cylinder chambers of a variable-speed pump controlled hydraulic system using structured recurrent neural network topologies where the rotational speed of the pumps, the position and the average velocity of the hydraulic actuator are used as their inputs. The paper elaborates the properties of such networks in extended time periods through detailed simulation- and experimental studies where black-box modeling approaches generally fail to yield acceptable performance. As alternative estimation techniques, both linear- and extended Kalman filters are considered in this paper. The estimation properties of the devised network models are comparatively evaluated and their potential application areas are discussed in detail.