Distributed Economic Model Predictive Control -- Addressing Non-convexity Using Social Hierarchies

TL;DR

This paper presents a distributed economic model predictive control algorithm that uses social hierarchies to address non-convexity, enabling scalable, distributed negotiation of control targets with proven convergence and stability.

Contribution

It introduces a novel hierarchy-based algorithm for DEMPC that does not require predefined set-points and guarantees convergence, stability, and scalability.

Findings

Algorithm effectively manages non-convex cost functions.

Proven guarantees of convergence and stability.

Outperforms basic parallel DEMPC in numerical tests.

Abstract

This paper introduces a novel concept for addressing non-convexity in the cost functions of distributed economic model predictive control (DEMPC) systems. Specifically, the proposed algorithm enables agents to self-organize into a hierarchy which determines the order in which control decisions are made. This concept is based on the formation of social hierarchies in nature. An additional feature of the algorithm is that it does not require stationary set-points that are known a priori. Rather, agents negotiate these targets in a truly distributed and scalable manner. Upon providing a detailed description of the algorithm, guarantees of convergence, recursive feasibility, and bounded closed-loop stability are also provided. Finally, the proposed algorithm is compared against a basic parallel distributed economic model predictive controller using an academic numerical example.