Efficient Robust Model Predictive Control using Chordality

TL;DR

This paper introduces a novel approach leveraging chordal structure to enhance the efficiency and parallelization of robust model predictive control optimization methods.

Contribution

It presents a new framework that exploits chordal structure for faster, distributed interior-point methods in robust MPC problems.

Findings

Significant reduction in computation time for robust MPC.

Effective parallelization of optimization algorithms.

Improved scalability for large-scale control problems.

Abstract

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The framework enables efficient parallel computations.