Coalitional Control for Self-Organizing Agents

TL;DR

This paper introduces a coalitional control framework for self-organizing multi-agent systems using model predictive control, enabling autonomous cooperation and negotiation among agents with competing objectives in large-scale systems.

Contribution

It presents a novel MPC-based approach with negotiation and benefit redistribution mechanisms for autonomous, cooperative multi-agent control in complex systems.

Findings

Effective formation of cooperative clusters demonstrated

Online adaptation of control structure based on system interactions

Application to power grid shows reduced frequency deviations

Abstract

Coalitional control is concerned with the management of multi-agent systems where cooperation cannot be taken for granted (due to, e.g., market competition, logistics). This paper proposes a model predictive control (MPC) framework aimed at large-scale dynamically-coupled systems whose individual components, possessing a limited model of the system, are controlled independently, pursuing possibly competing objectives. The emergence of cooperating clusters of controllers is contemplated through an autonomous negotiation protocol, based on the characterization as a coalitional game of the benefit derived by a broader feedback and the alignment of the individual objectives. Specific mechanisms for the cooperative benefit redistribution that relax the cognitive requirements of the game are employed to compensate for possible local cost increases due to cooperation. As a result, the structure of the overall MPC feedback can be adapted online to the degree of interaction between different parts of the system, while satisfying the individual interests of the agents. A wide-area control application for the power grid with the objective of minimizing frequency deviations and undesired inter-area power transfers is used as study case.