International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

Recursive parameter estimation for discrete-time model of an electro-hydraulic servo system with varying forgetting factor

R. Ghazali1, Y. M. Sam2, M. F. Rahmat2* and Zulfatman2
1Department of Mechatronic and Robotic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia.  2Department of Control and Instrumentation Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
Email: [email protected]

  •  Accepted: 01 November 2011
  •  Published: 23 November 2011



In general, an electro-hydraulic servo (EHS) system inherently suffers from parameter uncertainties and variation which makes the modeling and controller design complicated. To encounter those difficulties, recursive least square (RLS) is often used with advanced control strategy for position, force and pressure control of the EHS system. In this paper, a new RLS estimator with varying forgetting factor was proposed for the recursive parameter estimation process. The experimental work began with an organized procedure in developing a linear discrete-time model by emphasizing the offline identification process. Best fitting criterion, final prediction errors, minimum of loss function and correlation analysis were utilized to investigate the validity of the developed model. In recursive estimation, the proposed technique gave better estimations in terms of convergence speed and accuracy as compared to the conventional approach. Furthermore, it also showed that algorithm is more sensitive to parameter changes and improves the estimation results for each estimated parameter instead of using fixed forgetting factor.


Key words: Electro-hydraulic servo system, system identification, parameter estimation, recursive least square.