Sampling Strategies for Data-Driven Inference of Input-Output System Properties

TL;DR

This paper develops sampling strategies using gradient and saddle point flows to estimate system properties like $\\mathcal{L}^2$-gain and passivity from input-output data, applicable to both discrete and continuous time LTI systems.

Contribution

It introduces novel data-driven sampling methods based on dynamical systems for estimating system properties without explicit models.

Findings

Convergence of the proposed evolution equations is analyzed.

Sampling strategies effectively estimate system properties from data.

Applicable to both discrete and continuous time LTI systems.

Abstract

Due to their relevance in controller design, we consider the problem of determining the $\mathcal{L}^2$-gain, passivity properties and conic relations of an input-output system. While, in practice, the input-output relation is often undisclosed, input-output data tuples can be sampled by performing (numerical) experiments. Hence, we present sampling strategies for discrete time and continuous time linear time-invariant systems to iteratively determine the $\mathcal{L}^2$-gain, the shortage of passivity and the cone with minimal radius that the input-output relation is confined to. These sampling strategies are based on gradient dynamical systems and saddle point flows to solve the reformulated optimization problems, where the gradients can be evaluated from only input-output data samples. This leads us to evolution equations, whose convergence properties are then discussed in continuous time and discrete time.