Data-based Moving Horizon Estimation under Irregularly Measured Data

TL;DR

This paper proposes a data-driven moving horizon estimation method for linear systems with irregular measurements, relying on measured data rather than explicit models, and proves its stability.

Contribution

It introduces a novel sample-based estimation framework that does not depend on standard models and demonstrates its stability and practical application.

Findings

Proves practical robust exponential stability of the estimator.

Applies the method successfully to a gastrointestinal absorption system.

Handles irregular and sparse measurement data effectively.

Abstract

In this work, we introduce a sample- and data-based moving horizon estimation framework for linear systems. We perform state estimation in a sample-based fashion in the sense that we assume to have only few, irregular output measurements available. This setting is encountered in applications where measuring is expensive or time-consuming. Furthermore, the state estimation framework does not rely on a standard mathematical model, but on an implicit system representation based on measured data. We prove sample-based practical robust exponential stability of the proposed estimator under mild assumptions. Furthermore, we apply the proposed scheme to estimate the states of a gastrointestinal tract absorption system.