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Multi-agent networked linear dynamic systems have attracted attention ofresearchers in power systems, intelligent transportation, and industrialautomation. The agents might cooperatively optimize a global performanceobjective, resulting in social optimization, or try to satisfy their ownselfish objectives using a noncooperative differential game. However, in thesesolutions, large volumes of data must be sent from system states to possiblydistant control inputs, thus resulting in high cost of the underlyingcommunication network. To enable economically-viable communication, agame-theoretic framework is proposed under the \textit{communication cost}, or\textit{sparsity}, constraint, given by the number of communicatingstate/control input pairs. As this constraint tightens, the system transitionsfrom dense to sparse communication, providing the trade-off between dynamicsystem performance and information exchange. Moreover, using the proposedsparsity-constrained distributed social optimization and noncooperative gamealgorithms, we develop a method to allocate the costs of the communicationinfrastructure fairly and according to the agents' diverse needs for feedbackand cooperation. Numerical results illustrate utilization of the proposedalgorithms to enable and ensure economic fairness of wide-area control amongpower companies.

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