Neural-network-based fully distributed formation control for nonlinear multi-agent systems with event-triggered communication

This paper investigates the consensus-based formation control problem for multi-agent systems with unknown nonlinear dynamics. To achieve the desired formation, we propose two formation controllers to achieve the desired formation, one based on system states and the other on system outputs. The proposed controllers utilize adaptive gains to avoid global information and neural networks to estimate and compensate for nonlinearities. The proposed event-triggered schemes avoid continuous communication among agents and exclude the Zeno behavior. Stability analysis reveals that formation errors are bounded, and numerical simulations are used to validate the effectiveness of the proposed approaches.