A Greedy Data Collection Scheme For Linear Dynamical Systems

TL;DR

This paper introduces a heuristic data collection method for linear dynamical systems that optimizes information gain in both frequency and time domains, improving model reliability especially with limited and noisy data.

Contribution

It presents a novel greedy data collection scheme that enhances data quality for system modeling, addressing limitations of existing methods.

Findings

Method improves data quality in limited experiments

Robust against noisy data

Effective in both frequency and time domains

Abstract

Mathematical models are essential to analyze and understand the dynamics of complex systems. Recently, data-driven methodologies have got a lot of attention which is leveraged by advancements in sensor technology. However, the quality of obtained data plays a vital role in learning a good and reliable model. Therefore, in this paper, we propose an efficient heuristic methodology to collect data both in the frequency domain and time-domain, aiming at the best possible information gain from limited experimental data. The efficiency of the proposed methodology is illustrated by means of several examples, and also, its robustness in the presence of noisy data is shown.