Robust Distributed and Localized Model Predictive Control

TL;DR

This paper introduces a scalable, robust distributed model predictive control framework for large-scale linear systems, enabling local control with disturbance robustness and minimal information exchange.

Contribution

It presents the first scalable distributed MPC algorithm that handles additive disturbances using System Level Synthesis, with complexity independent of system size.

Findings

Controllers require only local information exchange

Computational complexity is independent of system size

First robust distributed MPC for large-scale systems with disturbances

Abstract

We present a robust Distributed and Localized Model Predictive Control (rDLMPC) framework for large-scale structured linear systems. The proposed algorithm uses the System Level Synthesis to provide a distributed closed-loop model predictive control scheme that is robust to exogenous disturbances. The resulting controllers require only local information exchange for both synthesis and implementation. We exploit the fact that for polytopic disturbance constraints, SLS- based distributed control problems have been shown to have structure amenable for distributed optimization techniques. We show that similar to the disturbance-free DLMPC algorithm, the computational complexity of rDLMPC is independent of the size of the global system. To the best of our knowledge, robust DLMPC is the first MPC algorithm that allows for the scalable distributed computation of distributed closed-loop control policies in the presence of additive disturbances.