Input-Output Data-Driven Representation: Non-Minimality and Stability

TL;DR

This paper demonstrates that data-driven output prediction models inherently contain stable latent poles, and leverages this to design controllers and estimators without pole elimination, simplifying data-driven control design.

Contribution

It proves the stability of latent poles in data-driven models using Moore-Penrose inverses, enabling direct controller and estimator design from data.

Findings

Latent poles in data-driven models are guaranteed stable.

Constructed a stabilizable and detectable realization from data.

Designed an output feedback LQR controller and unknown input estimator.

Abstract

Many recent data-driven control approaches for linear time-invariant systems are based on finite-horizon prediction of output trajectories using input-output data matrices. When applied recursively, this predictor forms a dynamic system representation. This data-driven representation is generally non-minimal, containing latent poles in addition to the system's original poles. In this article, we show that these latent poles are guaranteed to be stable through the use of the Moore-Penrose inverses of the data matrices, regardless of the system's stability and even in the presence of small noise in data. This result obviates the need to eliminate the latent poles through procedures that resort to low-rank approximation in data-driven control and analysis. It is then applied to construct a stabilizable and detectable realization from data, from which we design an output feedback linear quadratic regulator (LQR) controller. Furthermore, we extend this principle to data-driven inversion, enabling asymptotic unknown input estimation for minimum-phase systems.