Distributed Nash Equilibrium Seeking Under Event-Triggered Mechanism

This brief studies distributed event-triggered law design of the Nash equilibrium seeking for noncooperative games under strongly connected graphs. Each player is desired to maximize its payoff function by updating its own action based on other players' actions. An event-triggered law is proposed, and each player estimates all other players' actions and exchanges the estimated information with their neighbors only when the triggering conditions are satisfied. Theoretical demonstration is presented to verify that the actions and estimates of players all converge to the unique Nash equilibrium under the proposed event-triggered law and Zeno behavior is excluded. Simulations are provided to show the effectiveness of the main results.