# On Separable Quadratic Lyapunov Functions for Convex Design of Distributed Controllers

## TL;DR

This paper introduces a convex approach for designing distributed controllers using separable quadratic Lyapunov functions, improving feasibility and performance over diagonal-based methods, with practical procedures and numerical validation.

## Contribution

It generalizes previous diagonal Lyapunov function methods to separable quadratic functions, enabling better distributed control design.

## Key findings

- Convex restrictions improve controller feasibility.
- Separable quadratic Lyapunov functions enhance performance.
- Numerical examples validate the proposed approach.

## Abstract

We consider the problem of designing a stabilizing and optimal static controller with a pre-specified sparsity pattern. Since this problem is NP-hard in general, it is necessary to resort to approximation approaches. In this paper, we characterize a class of convex restrictions of this problem that are based on designing a separable quadratic Lyapunov function for the closed-loop system. This approach generalizes previous results based on optimizing over diagonal Lyapunov functions, thus allowing for improved feasibility and performance. Moreover, we suggest a simple procedure to compute favourable structures for the Lyapunov function yielding high-performance distributed controllers. Numerical examples validate our results.