Suboptimal estimation of systems with large parameter uncertainties

Abstract

The design of a filter that must estimate the state of a system when large parameter uncertainties in the plant dynamics or plant and measurement noise covariance matrices are present is considered. Filter designs are evaluated relative to the trade off between filter sensitivity to the uncertain parameters and minimum mean-square estimation error. This is accomplished by reformulating the estimation problem as an optimization problem with vector-valued performance index. The dominant design has form similar to the Kalman-Bucy filter, and employ open-loop compensation for the uncertain parameters.