Worst Case Learning for H ∞ Consensus for Heterogeneous Multiagent Systems: A Resource-Constrained Game Approach.
This article investigates heterogeneous high-order nonlinear multiagent systems (MASs) under an event-triggered mechanism (ETM) and proposes an approximately optimal consensus protocol derived by solving the Hamilton-Jacobi-Isaacs (HJI) equation with the proposed actor-critic-identifier (ACI) learning framework.