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

Date

2014-05-01

Author

KILIÇ, Ergin
Dölen, Melik
Çalışkan, Hakan
Koku, Ahmet Buğra
Balkan, Raif Tuna

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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.

Subject Keywords

Control and Systems Engineering, Electrical and Electronic Engineering, Applied Mathematics, Computer Science Applications

URI

https://hdl.handle.net/11511/37207

Journal

CONTROL ENGINEERING PRACTICE

DOI

https://doi.org/10.1016/j.conengprac.2014.01.008

Collections

Department of Mechanical Engineering, Article

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Citation Formats

E. KILIÇ, M. Dölen, H. Çalışkan, A. B. Koku, and R. T. Balkan, “Pressure prediction on a variable-speed pump controlled hydraulic system using structured recurrent neural networks,” CONTROL ENGINEERING PRACTICE, pp. 51–71, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37207.