Decentralized decision making for networks of uncertain systems

TL;DR

This paper introduces a decentralized approach for cooperative distributed model predictive control that enhances scalability and privacy by limiting communication to state bounds, enabling efficient solutions for large-scale uncertain systems.

Contribution

It proposes a novel decentralized problem synthesis scheme that reduces communication requirements and preserves privacy while maintaining solution quality.

Findings

The method achieves solutions close to centralized MPC.

It significantly reduces computational effort.

The approach is scalable and preserves privacy.

Abstract

Distributed model predictive control (MPC) has been proven a successful method in regulating the operation of large-scale networks of constrained dynamical systems. This paper is concerned with cooperative distributed MPC in which the decision actions of the systems are usually derived by the solution of a system-wide optimization problem. However, formulating and solving such large-scale optimization problems is often a hard task which requires extensive information communication among the individual systems and fails to address privacy concerns in the network. Hence, the main challenge is to design decision policies with a prescribed structure so that the resulting system-wide optimization problem to admit a loosely coupled structure and be amendable to distributed computation algorithms. In this paper, we propose a decentralized problem synthesis scheme which only requires each system to communicate sets which bound its states evolution to neighboring systems. The proposed method alleviates concerns on privacy since this limited communication scheme does not reveal the exact characteristics of the dynamics within each system. In addition, it enables a distributed computation of the solution, making our method highly scalable. We demonstrate in a number of numerical studies, inspired by engineering and finance, the efficacy of the proposed approach which leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.