Dynamic event-triggered adaptive NN consensus control for nonlinear multiagent systems with unknown measurement sensitivity

In this paper, the event-triggered adaptive neural network (NN) asymptotic consensus control problem is studied for nonlinear multi-agent systems (MASs) with unknown measurement sensitivity. A new distributed filter is designed to estimate the leader and solve the nondifferential problem of virtual controllers caused by the noncontinuous consensus error. The unknown nonlinear dynamics and measurement sensitivity are handled by using the NN approximators and adaptive estimation method, respectively. To avoid unnecessary data transmissions, a dynamic event-triggered mechanism (DETM) composed of the measurement and control channels is designed. Then based on backstepping technique, an asymptotic consensus controller is obtained. It is proved that the closed-loop signals are all bounded, and the consensus error asymptotically converges to the origin. Finally, to show the effectiveness of the developed control method, we apply the developed control method to multiple unmanned surface vehicles (USVs).