Behavioral Theory for Stochastic Systems? A Data-driven Journey from Willems to Wiener and Back Again

TL;DR

This paper extends behavioral systems theory to stochastic linear systems using Polynomial Chaos Expansions, enabling data-driven control and analysis by linking series coefficients with system behavior.

Contribution

It introduces a stochastic fundamental lemma for linear systems that integrates PCE with behavioral theory, broadening the formal basis for data-driven stochastic control.

Findings

Behavior of L^2-random variables is equivalent to behavior of PCE coefficients.

Series expansions enable behavioral characterization of stochastic systems.

The stochastic fundamental lemma facilitates numerically tractable data-driven control.

Abstract

The fundamental lemma by Jan C. Willems and co-workers, which is deeply rooted in behavioral systems theory, has become one of the supporting pillars of the recent progress on data-driven control and system analysis. This tutorial-style paper combines recent insights into stochastic and descriptor-system formulations of the lemma to further extend and broaden the formal basis for behavioral theory of stochastic linear systems. We show that series expansions -- in particular Polynomial Chaos Expansions (PCE) of $L^2$-random variables, which date back to Norbert Wiener's seminal work -- enable equivalent behavioral characterizations of linear stochastic systems. Specifically, we prove that under mild assumptions the behavior of the dynamics of the $L^2$-random variables is equivalent to the behavior of the dynamics of the series expansion coefficients and that it entails the behavior composed of sampled realization trajectories. We also illustrate the short-comings of the behavior associated to the time-evolution of the statistical moments. The paper culminates in the formulation of the stochastic fundamental lemma for linear (descriptor) systems, which in turn enables numerically tractable formulations of data-driven stochastic optimal control combining Hankel matrices in realization data (i.e. in measurements) with PCE concepts.