Event-triggered funnel control for network systems with unknown dynamic leader based on BP neural networks

This paper studies the cooperative adaptive funnel tracking control problem for a class of uncertain nonlinear nonstrict feedback multi-agent systems with nonlinear faults, and the dynamic leader has nonlinear unknown function. By designing two value functions and combining with the back propagation neural networks, an event-triggered strategy that can be adjusted automatically is designed to reduce the waste of communication bandwidth. The Butterworth low-pass filter signal is introduced to solve the non-smooth function problems. In addition, the radial basis function neural networks are used to approximate unknown functions in the system. Then, an event-triggered control protocol is constructed, which can achieve the tracking performance and all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results confirm the effectiveness of the proposed control scheme.