Aggregative control of large populations of noncooperative agents

We consider the problem to control a large population of noncooperative heterogeneous agents, each with strongly convex cost function depending on the average population state and convex constraints, towards an aggregative Nash equilibrium. We assume a minimal information structure through which a central controller can broadcast incentive signals to control the decentralized optimal responses of the agents. We propose a dynamic controller that, based on fixed point operator theory arguments, ensures global convergence if a sufficient condition on the matrix parameter defining the cost functions holds, yet independently on the convex constraints.