Informativity for centralized design of distributed controllers for networked systems

TL;DR

This paper addresses the design of distributed controllers for networked systems using data-driven methods, focusing on achieving sparse controllers that respect the system's distributed structure and information constraints.

Contribution

It introduces a data-driven approach for designing distributed controllers with specified block structures and proposes an algorithm to find maximally sparse controllers if it converges.

Findings

Algorithm can produce maximally sparse controllers

Method accounts for partial state measurements

Applicable to multi-agent networked systems

Abstract

Recent work in data-driven control has led to methods that find stabilizing controllers directly from measurements of an unknown system. However, for multi-agent systems we are often interested in finding controllers that take their distributed nature into account. For instance, the full state might not be available for feedback at every agent. In order to deal with such information, we consider the problem of finding a feedback controller with a given block structure based on measured data. Moreover, we provide an algorithm that, if it converges, leads to a maximally sparse controller.