Distributed Event-Triggered Optimal Consensus for Nonlinear MASs Under Switching Topologies

This article investigates the optimal consensus problem for a class of nonlinear multiagent systems (MASs) under an event-triggered mechanism (ETM) and switching topologies. By employing an adjusted backstepping technique, a more specific form of the backstepping optimal consensus protocol is presented, enabling the designed actor–critic networks to approximate an explicit target. Due to the incomplete knowledge of the system dynamics, a novel adaptive identifier is developed to approximate unknown nonlinear functions. Moreover, the energy of the MAS is taken into account when designing the dynamic thresholds. Specifically, two novel thresholds are introduced, which update alternately and correspond to fast and slow time scales, respectively. These specialized designs ensure the prevention of Zeno behavior and enable events to be triggered more adaptively. The optimal consensus of the MAS is guaranteed through a mathematical proof using a common Lyapunov function, ensuring that Zeno behavior is avoided. Finally, a simulation is provided to validate the effectiveness of the proposed control strategy.