Constrained maximum likelihood estimation for state space sampled-data models

Abstract

The Expectation-Maximization algorithm is applied in this paper to estimate state-space sampled-data models including constraints on the location of the poles. Linear quadratic matrix inequalities are used as constraints to obtain a model that preserves properties of the continuous time system, such as stability or damping characteristics. The results of the algorithm are shown in a simulation study.