Moving Horizon State Estimation of Discrete Time Systems

Abstract

This work considers the moving horizon estimation (MHE) problem. Most of todays applied or developed control schemes assume that the state of the controlled system is known explicitly. In reality the system states often cannot be measured directly; instead, they must be estimated from available output measurements. The traditional state estimation approach for linear systems under the in uence of noise is the Kalman Filter (KF). However, this approach does not allow to consider constraints on the noise terms or the states, that could improve the estimates. Also the extension to nonlinear systems is not possible without linearization or other approximations.