Application of Extended Kalman Filter in a Forced-Feedback Metering Poppet Valve System

This work focuses on an accurate Extended Kalman Filter (EKF) estimator, which is applied in a forced-feedback metering poppet valve system (FFMPVS). The EKF estimator is used to estimate the position and velocity of the main poppet valve, the pilot poppet valve and the piston through using the control volume pressure, the load pressure and the pressure between the pilot poppet and the actuator housing, which are all disturbed by noise. The EKF estimator takes advantage of its recursive optimal state estimation to estimate the states of this metering poppet valve, which is a non-linear, time-variant dynamical system in real time. The EKF estimator has robustness to parameter variations and ability to filter measurement noises. It is shown that the EKF estimator tracks the states confidently and promptly for both the steady-state and transient performance, at the same time, the EKF estimator also filters the noise of the measured pressures.