Data-driven Distributed and Localized Model Predictive Control

TL;DR

This paper introduces a novel data-driven distributed control algorithm, D³LMPC, designed for large-scale systems like IoT, enabling scalable, local, and stable control directly from trajectory data.

Contribution

The paper presents D³LMPC, a new data-driven distributed MPC method based on SLS that guarantees stability, scalability, and minimal data requirements independent of system size.

Findings

Algorithm guarantees recursive feasibility and asymptotic stability.

Demonstrates optimality and scalability in simulations.

Data requirements are independent of system size.

Abstract

Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized directly from trajectory data. Our method, data-driven Distributed and Localized Model Predictive Control (D$^3$LMPC), builds upon the data-driven System Level Synthesis (SLS) framework, which allows one to parameterize \emph{closed-loop} system responses directly from collected open-loop trajectories. The resulting model-predictive controller can be implemented with distributed computation and only local information sharing. By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system. Moreover, we show that our algorithm enjoys theoretical guarantees for recursive feasibility and asymptotic stability. Finally, we also demonstrate the optimality and scalability of our algorithm in a simulation experiment.