On the Optimal Control of Relaxation Systems

TL;DR

This paper derives analytical solutions for H-infinity optimal control problems in relaxation systems, highlighting their structured, sparse controllers suitable for large-scale applications like electrical networks and linear regression.

Contribution

It introduces analytical solutions for optimal control of relaxation systems and demonstrates their effectiveness in large-scale, sparse control applications.

Findings

Controllers are relaxation systems and often sparse.

Effective in large-scale electrical network control.

Applicable to linear regression problems.

Abstract

The relaxation systems are an important subclass of the passive systems that arise naturally in applications. We exploit the fact that they have highly structured state-space realisations to derive analytical solutions to some simple H-infinity type optimal control problems. The resulting controllers are also relaxation systems, and often sparse. This makes them ideal candidates for applications in large-scale problems, which we demonstrate by designing simple, sparse, electrical circuits to optimally control large inductive networks and to solve linear regression problems.