Game-Theoretic Multi-Agent Control and Network Cost Allocation under Communication Constraints
For multi-agent systems in power grids and transportation, this work provides a way to balance performance and communication costs while ensuring economic fairness, though the approach is incremental.
This paper addresses the trade-off between dynamic system performance and communication cost in multi-agent networked systems, proposing a game-theoretic framework under a sparsity constraint. The method enables fair allocation of communication infrastructure costs among agents, demonstrated in wide-area power system control.
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.