Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems

TL;DR

This paper proposes a data-driven moving horizon estimation method for linear discrete-time systems that guarantees stability without system identification, even with noisy measurements, demonstrated through simulations.

Contribution

It introduces a novel data-based MHE approach that does not require system identification and proves its stability under measurement noise.

Findings

Proves robust global exponential stability of the data-based MHE.

Demonstrates effectiveness through simulation example.

Abstract

This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data without employing any system identification step. Robust global exponential stability of the data-based MHE is proven under standard assumptions for the case where the online output measurements are corrupted by some non-vanishing measurement noise. A simulation example illustrates the behavior of the data-based MHE scheme.