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EKF for State Estimation of Electro-Hydraulic Servo Systems | ||
| Engineering and Technology Journal | ||
| Volume 31, Issue 21, 2013, Pages 4044-4056 PDF (1.17 M) | ||
| DOI: 10.30684/etj.2013.189596 | ||
| Author | ||
| Firas A. Raheem | ||
| Control and Systems Department, University of Technology / Baghdad | ||
| Abstract | ||
| The goal of this paper is to estimate the states of hydraulic actuator. The system is highly nonlinear and one therefore cannot directly use any linear system tools for estimation. The standard Kalman filter addresses the problem of estimating the state of a linear stochastic controlled process. But the dynamic model of the hydraulic actuator to be estimated is non-linear, therefore; for the standard Kalman filter to be applied to such nonlinear system the nonlinear system is linearized first and then the recursive equations of the standard Kalman filter are applied for time update. The Kalman filter which tackles the estimation problem of linearized nonlinear process and linearizes about the current mean and covariance is referred extended Kalman filter (EKF). The entire state estimated system has been modeled using MATLAB software. The EKF could successfully estimates the hydraulic system variables in spite of its high nonlinearity. The robustness of the EKF is examined as the system parameters are changed. Three critical parameters are selected which actually suffer varying; the spring stiffness, the spool valve time constant and the bulk of modulus. The stiffness, spool time constant and the bulk of modulus have been hypothetically increased up to 100%, 300% and 200% over their nominal values, respectively. The results show that the EKF is insensitive to both spring stiffness and the bulk of modulus, while its performance degrades as changing the value of spool time constant. | ||
| Keywords | ||
| Kalman Filter; State Estimation; Hydraulic Actuator | ||
| References | ||
|
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