A New Identification Framework For Off-Line Computation of Moving-Horizon Observers

TL;DR

This paper introduces a novel nonlinear identification framework that enhances off-line computation of moving-horizon observers by combining nonlinear approximation with efficient quadratic programming, improving state estimation accuracy.

Contribution

The paper presents a new identification approach that integrates nonlinear approximators with quadratic programming for improved off-line observer estimation.

Findings

Bound on estimation error established

Scheme demonstrated with two state estimation examples

Enhanced computational efficiency achieved

Abstract

In this paper, a new nonlinear identification framework is proposed to address the issue of off-line computation of moving-horizon observer estimate. The proposed structure merges the advantages of nonlinear approximators with the efficient computation of constrained quadratic programming problems. A bound on the estimation error is proposed and the efficiency of the resulting scheme is illustrated using two state estimation examples.