# Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis

## TL;DR

This paper introduces a novel distributed data-driven predictive control method leveraging dissipativity in the behavioral framework, enabling effective network-wide control design through local QdF conditions and set-theoretic optimization.

## Contribution

It proposes a unified, data-driven distributed control design framework using dissipativity as a behavior, integrating local QdF conditions for global optimization.

## Key findings

- Effective control performance achieved in interconnected systems.
- Distributed optimization based on local QdF conditions is feasible.
- The approach is validated through a practical example.

## Abstract

This paper presents a distributed data-driven predictive control (DDPC) approach using the behavioral framework. It aims to design a network of controllers for an interconnected system with linear time-invariant (LTI) subsystems such that a given global (network-wide) cost function is minimized while desired control performance (e.g., network stability and disturbance rejection) is achieved using dissipativity in the quadratic difference form (QdF). By viewing dissipativity as a behavior and integrating it into the control design as a virtual dynamical system, the proposed approach carries out the entire design process in a unified framework with a set-theoretic viewpoint. This leads to an effective data-driven distributed control design, where the global design goal can be achieved by distributed optimization based on the local QdF conditions. The approach is illustrated by an example throughout the paper.