Event‐Triggered Distributed Robust Minimax Control for Networked Multiplayer Systems

ABSTRACT Networked multiplayer systems often encounter uncertainties and asymmetric information due to complex communication topologies, posing significant challenges for robust control design. To address these issues while reducing communication load and improving update efficiency, this paper proposes an event‐triggered robust minimax control framework, based on a modified reinforcement learning algorithm. Players are classified into reachable and unreachable sets; the influence of unreachable players is modeled as bounded uncertainty, while reachable but non‐adjacent players are treated as adversarial perturbations within a robust minimax framework. The stability of the multiplayer system under the proposed strategy is rigorously proven. An event‐triggered mechanism is incorporated into the control update process, allowing asynchronous updates while preserving system stability. The algorithm combines recursive least squares‐based policy evaluation with multiple policy improvement to accelerate convergence. Simulation results on a helicopter system verify the effectiveness and practicality of the proposed approach.