On data usage and predictive behavior of data-driven predictive control with 1-norm regularization

TL;DR

This paper analyzes how data-driven predictive control with 1-norm regularization uses data and predicts system behavior, enabling data removal and comparison with true system structure to evaluate control effectiveness.

Contribution

It introduces methods for offline data removal and compares data usage with true system structure in DPC with 1-norm regularization.

Findings

Enables offline removal of unused data

Allows comparison of data usage with true system structure

Assists in assessing control scheme suitability

Abstract

We investigate the data usage and predictive behavior of data-driven predictive control (DPC) with 1-norm regularization. Our analysis enables the offline removal of unused data and facilitates a comparison between the identified symmetric structure and data usage against prior knowledge of the true system. This comparison helps assess the suitability of the DPC scheme for effective control.