Application of Extended Kalman Filter in a Metering Poppet Valve System

Abstract

This work focuses on an accurate Extended Kalman Filter (EKF) estimator, which is applied to a forced-feedback metering poppet valve system (FFMPVS). The EKF estimator is used to estimate the position and velocity of the main poppet valve, position and velocity of the pilot poppet valve and pressures within the pilot stage of the valve. The EKF estimator takes advantage of its recursive optimal state estimation to estimate the states of this metering poppet valve by using one pressure signal measurement. The results from the EKF are compared with the simulation results from the model and also compared with the states which can be measured from the physical system set up in the lab. It is shown that the EKF estimator tracks the states accurately for both the steady-state and transient performance. The EKF estimator has robustness to parameter variations. It is shown specifically that the EKF estimator has robustness to an example of model uncertainty, variations in the spring stiffness parameter.