A general partitioning strategy for non-centralized control

TL;DR

This paper presents a novel partitioning framework for non-centralized control of large-scale systems, combining fundamental system units with aggregative procedures, and introduces a global network metric to optimize system decomposition.

Contribution

It introduces a generalized partitioning approach with a global network metric and a flexible aggregation process, applicable to various large-scale control systems.

Findings

Reduces computation time in distributed control.

Maintains or improves control performance.

Applicable to linear and hybrid systems.

Abstract

Partitioning is a fundamental challenge for non-centralized control of large-scale systems, such as hierarchical, decentralized, distributed, and coalitional strategies. The problem consists of finding a decomposition of a network of dynamical systems into system units for which local controllers can be designed. Unfortunately, despite its critical role, a generalized approach to partitioning applicable to every system is still missing from the literature. This paper introduces a novel partitioning framework that integrates an algorithmic selection of fundamental system units (FSUs), considered indivisible entities, with an aggregative procedure, either algorithmic or optimization-based, to select composite system units (CSUs) made of several FSUs. A key contribution is the introduction of a global network metric, the partition index, which quantitatively balances intra- and inter-CSU interactions, with a granularity parameter accounting for the size of CSUs, allowing for their selection at different levels of aggregation. The proposed method is validated through case studies in distributed model predictive control (DMPC) for linear and hybrid systems, showing significant reductions in computation time and cost while maintaining or improving control performance w.r.t. conventional strategies.