Game-Theoretic Multi-Agent Control and Network Cost Allocation under Communication Constraints

TL;DR

This paper introduces a game-theoretic framework for multi-agent control under communication constraints, balancing system performance and communication costs, and providing fair cost allocation among agents.

Contribution

It proposes a novel sparsity-constrained game-theoretic approach for multi-agent control that optimizes communication efficiency and fairness.

Findings

Effective trade-off between control performance and communication cost.

Algorithms enable fair cost allocation based on agents' needs.

Numerical results demonstrate economic fairness in power system control.

Abstract

Multi-agent networked linear dynamic systems have attracted attention of researchers in power systems, intelligent transportation, and industrial automation. The agents might cooperatively optimize a global performance objective, resulting in social optimization, or try to satisfy their own selfish objectives using a noncooperative differential game. However, in these solutions, large volumes of data must be sent from system states to possibly distant control inputs, thus resulting in high cost of the underlying communication network. To enable economically-viable communication, a game-theoretic framework is proposed under the \textit{communication cost}, or \textit{sparsity}, constraint, given by the number of communicating state/control input pairs. As this constraint tightens, the system transitions from dense to sparse communication, providing the trade-off between dynamic system performance and information exchange. Moreover, using the proposed sparsity-constrained distributed social optimization and noncooperative game algorithms, we develop a method to allocate the costs of the communication infrastructure fairly and according to the agents' diverse needs for feedback and cooperation. Numerical results illustrate utilization of the proposed algorithms to enable and ensure economic fairness of wide-area control among power companies.