Distributed Design of Glocal Controllers via Hierarchical Model Decomposition

TL;DR

This paper introduces a scalable distributed control design method for large-scale network systems using hierarchical model decomposition to create glocal controllers that integrate global and local dynamics.

Contribution

It presents a novel hierarchical model decomposition approach enabling independent design of glocal controllers for large-scale networks.

Findings

Hierarchical model decomposition facilitates distributed controller design.

The method effectively manages global and local dynamics in power grid examples.

A clustering method suitable for the approach improves scalability.

Abstract

This paper proposes a distributed design method of controllers having a glocal (global/local) information structure for large-scale network systems. Distributed design, independent design of all subcontrollers that constitute a structured controller, facilitates scalable controller synthesis. While existing distributed design methods confine attention to the decentralized or distributed information structures, this study addresses distributed design of glocal-structured controllers. Glocal control exploits the nature that network system's behavior can typically be represented as a superposition of spatially local fluctuations and global interarea oscillations by incorporating a global coordinating subcontroller with local decentralized subcontrollers. The key idea to distributed design of glocal controllers is to represent the original network system as a hierarchical cascaded system composed of reduced-order models representing the global and local dynamics, referred to as hierarchical model decomposition. Distributed design is achieved by independently designing and implementing subcontrollers for the reduced-order models while preserving the cascade structure. This paper provides a condition for existence of the hierarchical model decomposition, a specific representation of the hierarchical system, a clustering method appropriate for the proposed approach, and a robust extension. Numerical examples of a power grid evidence the practical relevance of the proposed method.