NONLINEAR ROBUST ADAPTIVE EKF FOR IDENTIFICATION OF EMAs PARAMETERS IN THE PRESENCE OF SENSOR FAULTS
An Extended Kalman filter (EKF) is designed to estimate the parameters of the electro-mechanical actuator (EMA). If the measurements are not reliable because of any kind of malfunction in the estimation system, the filter gives inaccurate results and diverges by time. For the presence of measurement faults, a Nonlinear Robust Adaptive EKF with the filter gain correction based on the evaluation of the posterior probability of the normal operation of system, given for current measurement is proposed. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. In the proposed filtration algorithm, the filter gain is corrected by multiplying with the mentioned posterior probability, which plays the role of the weight coefficients to the innovation vector. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behavior. The developed Nonlinear Robust Adaptive EKF is applied for the parameter identification process of an EMA. The performance of the proposed filter is tested for the different types of measurement faults; instantaneous abnormal measurements, continuous bias at measurements, measurement noise increment and fault of zero output.