Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems

TL;DR

This paper introduces a robust data-driven moving horizon estimation method for linear discrete-time systems that operates solely on offline data, ensuring stability despite noise, and is validated through simulations.

Contribution

It presents a novel robust data-driven MHE scheme that does not require system identification and guarantees stability under noisy conditions.

Findings

Proves practical robust exponential stability under noise.

Demonstrates effectiveness through simulation comparisons.

Shows advantages over standard model-based MHE.

Abstract

In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by non-vanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared to a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.