The Use of Inequality Constraints on Unknown Parameters as Pseudomeasurement in System Identification

Abstract

An approach is proposed to identify unknown system parameters which are known a priori to be constrained by their own upper and lower bounds. The inequality constraints are incorporated in the original observation data as pseudomeasurement. The identification is performed within the Kalman filtering framework. Several numerical experiments are provided.