Estimation of model states for a hydraulic valve system using an extended Kalman filter
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Pilot valves are considered as one of most common types of valves in the industry today. They are widely-used because their design is simple, they are available in wide range of number of ports, they have high efficiency and smooth flow rate, and they are easy to operate and maintained. They are used in many applications like aircraft flight control, machinery, and automobiles etc. In this thesis, an extended Kalman filter (EKF) has been implemented practically to estimate the spool displacement of the main valve in a hydraulic system. The system includes a supply pressure feeding a three-way pilot valve, which controls a four-way main valve. Based on the mathematical equations, Matlab/Simulink models of the pilot valve and system components were made to compare with experimental data and used in EKF. Extended Kalman filter algorithm has employed to estimate the displacement of the main valve in presence of random noise and distortions again taking into account the noise measurement. EKF being an optimal estimator tracks the displacement with noise and distortion quite accurately. Adaptive tracking of movement of the main valve in the Hydraulic system can easily be done using this algorithm.