Minimum Input Design for Direct Data-driven Property Identification of Unknown Linear Systems

TL;DR

This paper introduces a direct data-driven method for property identification of unknown linear systems, establishing minimal input design conditions and demonstrating data efficiency advantages over traditional model-based approaches.

Contribution

It presents a novel necessary and sufficient condition for minimum input design using input sectional data for property identification in linear systems.

Findings

Characterizes when input data is sufficiently rich for property ID

Shows many structural properties can be identified without explicit system models

Quantifies data efficiency benefits of direct data-driven analysis

Abstract

In a direct data-driven approach, this paper studies the {\em property identification(ID)} problem to analyze whether an unknown linear system has a property of interest, e.g., stabilizability and structural properties. In sharp contrast to the model-based analysis, we approach it by directly using the input and state feedback data of the unknown system. Via a new concept of sufficient richness of input sectional data, we first establish the necessary and sufficient condition for the minimum input design to excite the system for property ID. Specifically, the input sectional data is sufficiently rich for property ID {\em if and only if} it spans a linear subspace that contains a property dependent minimum linear subspace, any basis of which can also be easily used to form the minimum excitation input. Interestingly, we show that many structural properties can be identified with the minimum input that is however unable to identify the explicit system model. Overall, our results rigorously quantify the advantages of the direct data-driven analysis over the model-based analysis for linear systems in terms of data efficiency.