Neural Network-Based Control for Nonlinear Multi-Agent Systems via Dynamic Event-Triggered Mechanism

In the case where the multi-agent network contains a directed spanning tree and the topology between followers forms an undirected connected graph, this paper studies the consensus problem of nonlinear leader-following multi-agent systems with external disturbances. An adaptive neural network control strategy based on the states of neighboring agents is proposed. To address the resource constraints caused by the neural network and communication, dynamic event-triggered mechanism is introduced. Through rigorous theoretical analysis and proofs, it is demonstrated that the proposed control strategy can ensure that the multi-agent system converges to a stable equilibrium state under external disturbances and achieves state consensus.