The informativity approach to data-driven analysis and control

TL;DR

This paper introduces the informativity framework for data-driven analysis and control, demonstrating its effectiveness through various case studies involving noiseless and noisy data, and covering multiple control problems.

Contribution

It provides a comprehensive tutorial on the informativity approach, including theoretical foundations and practical case studies for data-driven control and analysis.

Findings

Effective in noiseless and noisy data scenarios

Applicable to controllability, stabilizability, and regulation problems

Utilizes quadratic matrix inequalities and behavioral systems theory

Abstract

The goal of this paper is to provide a tutorial on the so-called informativity framework for direct data-driven analysis and control. This framework achieves certified data-based analysis and control by assessing system properties and determining controllers for sets of systems unfalsified by the data. We will first introduce the informativity approach at an abstract level. Thereafter, we will report case studies where we highlight the strength of the framework in the context of various problems involving both noiseless and noisy data. In particular, we will treat controllability and stabilizability, and stabilization, linear quadratic regulation, and tracking and regulation using exact input-state measurements. Thereafter, we will treat dissipativity analysis, stabilization, and H_inf control using noisy input-state data. Finally, we will study dynamic measurement feedback stabilization using noisy input-output data. We will provide several examples to illustrate the approach. In addition, we will highlight the main tools underlying the framework, such as quadratic matrix inequalities in robust control and quadratic difference forms in behavioral systems theory.