Data-Based Analysis of Relative Degree and Zero Dynamics in Linear Systems

TL;DR

This paper introduces data-driven conditions to assess fundamental properties like relative degree and zero dynamics stability in linear systems, aiding controller design without explicit model identification.

Contribution

It provides novel data-based criteria for analyzing relative degree and zero dynamics stability in linear systems, applicable to both discrete and continuous-time data without requiring explicit system models.

Findings

Data-based conditions for relative degree assessment

Criteria for zero dynamics stability from data

Extension to continuous-time systems via sampling reconstructions

Abstract

Data-driven control offers a powerful alternative to traditional model-based methods, particularly when accurate system models are unavailable or prohibitively complex. While existing data-driven control methods primarily aim to construct controllers directly from measured data, our approach uses the available data to assess fundamental system-theoretic properties. This allows the informed selection of suitable control strategies without explicit model identification. We provide data-based conditions characterizing the (vector) relative degree and the stability of the zero dynamics, which are critical for ensuring proper performance of modern controllers. Our results cover both single- and multi-input/output settings of discrete-time linear systems. We further show how a continuous-time system can be reconstructed from three sampling discretizations obtained via Zero-order Hold at suitable sampling times, thus allowing the extension of the results to the combined data collected from these discretizations. All results can be applied directly to observed data sets using the proposed algorithms.