Self switched R-adaptive extended Kalman Filter based state estimation and mode determination for nonlinear hybrid systems

Abstract

An improved estimation method for a class of nonlinear hybrid systems has been proposed in this paper using a self-switched R-adaptive Extended Kalman Filter. The term `estimation of a hybrid system' implies state estimation as well as mode estimation of a plant. In hybrid systems, where modes are determined by the output variables, the mode determination may become erroneous due to inaccurately known or wrongly initialized measurement noise covariances. Innovation and residual based R-adaptive Extended Kaiman filters are employed here successfully for adapting true sensor noise covariance. A three tank system has been used in this work to demonstrate the effectiveness of the scheme. Simulation results show the effects on the performance of the estimator due to inaccurately or wrongly assigned magnitudes of sensor noise covariance using innovation based and residual based adaptive extended Kaiman filter. A comparison with an EKF based self-switched filter shows that the proposed A-EKF based self-switched filter is more robust.