Distributed Synthesis Using Accelerated ADMM

TL;DR

This paper introduces a convex distributed optimization algorithm using accelerated ADMM for synthesizing robust controllers in large-scale systems, enabling scalable control design respecting system interconnections.

Contribution

It develops a novel distributed synthesis method leveraging accelerated ADMM with a fast convergence rate for large-scale control problems.

Findings

Achieves $O(1/k^2)$ convergence rate with accelerated ADMM.

Enables scalable control synthesis respecting interconnection topology.

Demonstrates effectiveness in $ ext{H}_ ext{ extbf{ extit{infty}}}$ control design.

Abstract

We propose a convex distributed optimization algorithm for synthesizing robust controllers for large-scale continuous time systems subject to exogenous disturbances. Given a large scale system, instead of solving the larger centralized synthesis task, we decompose the problem into a set of smaller synthesis problems for the local subsystems with a given interconnection topology. Hence, the synthesis problem is constrained to the sparsity pattern dictated by the interconnection topology. To this end, for each subsystem, we solve a local dissipation inequality and then check a small-gain like condition for the overall system. To minimize the effect of disturbances, we consider the $\mathrm{H}_\infty$ synthesis problems. We instantiate the distributed synthesis method using accelerated alternating direction method of multipliers (ADMM) with convergence rate $O(\frac{1}{k^2})$ with $k$ being the number of iterations.