Distributed Synthesis of Gray-Box Distributed H2 Controllers

TL;DR

This paper introduces a scalable, privacy-preserving distributed method for synthesizing  controllers using gray-box models and ADMM, demonstrated on a power system test case.

Contribution

It develops a novel distributed  controller synthesis approach combining partial model knowledge and data, handling unknown dynamics without centralized computation.

Findings

Effective in simulations of IEEE 39-bus power system

Handles unknown dynamics and partial model knowledge

Operates in a fully distributed manner without a central server

Abstract

Distributed controller synthesis offers scalable and privacy-preserving control design, but typical state-of-the-art approaches either assume white-box models or resort to centralized synthesis. In this paper, we combine partially known model knowledge and an input-state dataset within a distributed gray-box scheme to design \(\mathcal{H}_2\) controllers. Our method can handle unknown dynamics and offers scalable synthesis. Each agent communicates with a set of neighbors determined by the physical coupling topology of the system such that we can apply the Alternating Direction Method of Multipliers (ADMM) to solve the problem iteratively in a fully distributed fashion (i.e., without a central server). The effectiveness and flexibility of the proposed approach is demonstrated in simulations of the IEEE 39-bus power system test case.